Employer Insight session: Environment Agency 

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Hi, uh, my name’s Pam Reynolds.

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I, um, I’m the water specific area of the water

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of the Environment Agency Training Skills

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senior, um, advisor.

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And, um, I was just gonna say

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that within those the next 30 minutes, we’re gonna kind

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of myself and my colleague Emily, that I’ll pass over

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because it’s about her really.

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Uh, we’re gonna kind of cover three kind of areas, sort

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of managing the transition from academia to industry, uh,

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skills developed in research,

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supporting your role at the Environment Agency,

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and the practicalities

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of a career in the environment agency as well.

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So we’ll try and get through those, so see

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how the conversation goes, see where we get up to.

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Um, yeah. And then we’ll open it up to questions.

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So I will now hand over to Emily to introduce yourself.

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Perfect. Can you hear me okay? So coming? Yes. Brilliant.

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Um, yeah. Hi. Hi, everyone. Um, my name’s Emily Fallon.

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Um, so yeah, I, um, I, I work in the Environment Agency.

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Um, I guess there’s a bit of information about the e the ea

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we’ll refer to it as, because we, we have an acronym,

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um, in the, in the agency.

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Um, we’re a Kango, um, so I guess we’re kind

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of an arms length body, um, funded party by government,

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but not run by government.

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Um, so we’re the environmental regulator,

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so we’re responsible for flood management, waste management,

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regulating land and water pollution, um,

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and looking at conservation as well.

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So, um, myself, um, uh, I sit in a,

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i I sit in a national water resources modeling team in

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the Environment Agency.

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Um, and so what, what that means is that I lead on

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and deliver work for a national project, um, which most,

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most people won’t know here, but it’s,

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it’s called the National Framework for Water Resources.

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And essentially what that does is assess our nation’s water

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needs in the future.

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Um, in reality, what that means is that I, I work

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and run lots of different consultancy projects.

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I, and we also develop

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and run, um, national water resource models in-house.

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Um, and those models provide a lot of data

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and evidence that underpin the project

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and, um, provide information for, for government and policy.

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Um, so what we’re essentially trying

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to do is quantitatively assess, um,

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our nation’s water needs in the future.

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So, um, what’s that deficit gonna look like in the 2050s?

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Um, spoiler alert, there will be a deficit in the 2050s.

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Um, uh, and yeah,

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and I guess how we start to address that, um, as,

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as a regulator, um,

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and working with water

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companies, how we face that in the future.

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Um, so I guess to do my role, I, I lead a very small team

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of, of one, so myself and another water resources modeler.

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Um, and in reality, I guess my role in the EA involves a lot

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of technical leading of, of work.

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So outlining kind of high level objectives of, of modeling,

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figuring out how to develop models or add new functionality

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and how, how we use our models

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to answer particular questions

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that government are interested in deciding what scenarios

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and things to run, kind of working out best approaches

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to solving kind of tech, tech technical problems.

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Working with experts to bring in the latest data evidence.

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Um, and we also provide a lot of expertise

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to other partners, um, and,

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and, um, people like the, the financial regulators, so, um,

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secretary of State, et cetera.

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Um, and in, in reality, a lot of, a lot of

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what I do is also project planning, managing resources,

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time, and I also line manage as well, um, I should say.

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So I moved into the EA about five years ago now.

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My first week, I always remember it

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because my first week was, um, the first week

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of covid lockdown, so it was a little bit traumatizing.

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Um, but I’ve actually had four different roles in the ea um,

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in, in the five years that I’ve, I’ve been here all kind

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of related to water and water resources.

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Um, so, and I’ve moved up a couple of, I guess, grades

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or levels in, in that time.

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Um, yeah.

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Thank you. Well, you’ve been busy.

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Um, can you tell us about, um, your previous academic career

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and maybe what inspired you to transition from academia into

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your current role or the environment agency?

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Yeah, I can, yeah. So my, my previous academic, so I,

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I actually did a, i in geology at the University of Bristol.

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Um, and I, and

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that included a year abroad at the University of of Oregon.

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So, um, a lot of, a lot of rocks.

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Um, and I, my master’s project was actually looking,

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it was kind of more geochemistry

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and experimental petrology,

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so essentially making little volcanoes at high in high

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pressure, high temperature experiments.

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Um, and then my PhD, I moved on

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to a PhD straight after that.

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Again, linked looking at, um, geochemistry based, um,

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but looking at sea floor volcanoes, so sea floor,

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massive sulfides, um, so that are produced

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by underwater volcanoes

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and more focused on mining, so the economic viability, um,

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of mining sea floor deposits,

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and then the environmental impact of,

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of mining that as well.

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Um, so that was about th three and a half, say three

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and a half, more like four years, to be honest.

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Um, and then moved into,

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and I had a, first, I had a year postdoc, so I had

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a knowledge transfer postdoc,

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which is basically bringing a lot of my environmental impact

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of mining knowledge into German government for a year.

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So I worked in Germany for a year, and then

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after that I had, um, three years of a nerc,

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um, fellowship.

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Uh, but I actually only did two years of a bit,

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but that postdoc was a bit of a shift back to what my PhD,

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um, ma my master’s was on.

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So I was looking more at volcanoes

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and experimental lab work, essentially with some modeling

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of geochemistry.

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But as I say, I left

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after the two years of the, the three year, um, funding.

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And you’re gonna ask, yeah, I think you asked me what,

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why the transition.

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Mm-hmm. Um, so yeah, so I think, oh,

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it’s a, it’s a loaded question.

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Um, why did I leave?

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Um, I think, I mean, I, I think it’s quite clear from a lot

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of my background, um, even my, from my PhD,

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I’ve always enjoyed application

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of research into real world problems.

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So I think a lot of people accumulate

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that academia can feel a bit abstract at times

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and a little bit niche.

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Um, and with only a few other few, a few other people, kind

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of like your little, couple of research teams

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around the world that really understand what you do.

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And I think as a result, it can sometimes feel a bit lonely

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if, if I’m honest, or I, I found it a little bit lonely.

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And I think, honestly, after my first postdoc

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and going into the NERC postdoc,

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I was just really quite burnt out and feeling pretty low.

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I’m just gonna be honest. I think I’d

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lost a bit of love for it.

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Um, and I think I was kind of craving a bit more stability.

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So one of the downsides of academia,

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and you’re lucky if you don’t have this impacting you,

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but there’s kind of the endless short term contracts

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of academia, the constant churning out of work, um,

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and moving around, but

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to different universities didn’t really

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appeal to me personally.

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Um, and I think, if I’m honest,

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I always felt a bit like an imposter in academia.

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I think it’s quite natural in research you feel like an

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imposter ’cause you’re kind of in unknown territory

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where you’re the only one of a handful

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of people who really know what you’re doing.

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But it’s could be quite exhausting

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for honest, if I was honest.

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Um, I felt like I had to constantly work to keep up.

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Um, so I didn’t have the best work life balance,

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I would say I envy people who can get that balance.

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Um, so yeah, it’s a little bit of a lot

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of those compounding factors.

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Um, I think I was craving working as, as in a team, um,

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and having a bit more of an impact in, in my work.

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So, which is ultimately why,

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why I came into the ea it was the

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hardest decision I ever had to make.

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I think leaving academia, um, especially,

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I was very lucky though that I had, um, to get

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that three year NERC postdoc

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after, um, straight away

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because I actually, I, it was completely,

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completely my choice to leave, which I think is quite rare.

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Um, so often the funding kind of runs out.

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Um, but yeah, there’s many, there was many pros

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and cons lists about leaving or staying.

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Um, but I, yeah, I think I’m, I’m very happy with it,

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the decision i, decision I made to, to leave.

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Um, but yeah, I was kind of worried that I’d spent a lot

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of years dedicated to this field,

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and I would, it was like, I was like throwing away a lot of

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that or feeling like I was gonna lose some freedom

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or flexibility or, um,

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and a lot of those things didn’t actually come

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in, come into fruition.

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I think a lot of the things I was worried about, um, yeah.

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And yeah, ended up applying for a job in the ea

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and as I say, I’ve worked up,

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I’ve moved up into different roles.

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But yeah, I love it

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and I, I don’t re I don’t regret it at all moving

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Into the ea.

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Appreciate Your honesty there

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though, with, with everything.

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Do, did you need to upskill then

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or retrain for the, a role in the,

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in the environment agency?

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I, I did, yes.

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And I think realist probably more so than other, you,

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if you can get a role in your field going into industry,

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that’s probably easier to do.

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I mean, it was kind of semi-related.

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Obviously I’ve worked kind of in the world of environment

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and mapping

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and geology, which kind of translates

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to an extent, but yes, I did.

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Um, yeah,

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and I think it realistically, most people would need to,

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even, even if not a full retrain, there’ll be something

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that you need to learn or upskill in.

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And when you’re moving into a role in industry, whether

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that be a particular sort of process

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or piece of software jargon, for example.

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Um, but yeah, I had to, I had to learn,

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and I’m still learning entirely new field, so I’m kind

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of learning water now.

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I often joke that it’s

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because I used to do a lot of vol, um,

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lava, volcano modeling.

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It’s like, oh, oh, now I’m,

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I’m modeling just a different fluid.

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I’m, I’m modeling water instead of, um, instead of lava.

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Um, but, uh, yeah, my,

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my background is obviously in geochemistry

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and bulk volcanology, so quite a shift.

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Um, but I had a lot of the, the underlying skills already,

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so I could program, I was, I could, um, a lot of the stats,

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big data analysis, figure making, mapping, report writing,

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publication writing, communicating, pre presenting,

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like I had a lot of the skill sets, the underlying skillset

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00:10:38.905 –> 00:10:41.605
to get into water and learn the field.

241
00:10:41.605 –> 00:10:43.605
And actually a lot of the people in our, um,

242
00:10:44.735 –> 00:10:47.555
in our water resources modeling team, not all

243
00:10:47.555 –> 00:10:48.715
of them have a water background.

244
00:10:48.775 –> 00:10:51.395
We, we often argue that it’s actually, in some ways,

245
00:10:51.395 –> 00:10:55.115
it’s sometimes easier to, to learn the water knowledge as,

246
00:10:55.455 –> 00:10:58.555
and if you have that already underlying skill set of, um,

247
00:10:58.825 –> 00:11:03.085
yeah, as I say, stats, programming, et cetera, those,

248
00:11:03.085 –> 00:11:04.165
those skills are actually harder

249
00:11:04.165 –> 00:11:06.525
to find in the industry, I would say.

250
00:11:07.145 –> 00:11:08.145
Um,

251
00:11:08.795 –> 00:11:09.795
Yeah. Yeah.

252
00:11:09.795 –> 00:11:11.525
Well linking with that then,

253
00:11:11.585 –> 00:11:13.725
so have your analy analytical

254
00:11:13.865 –> 00:11:16.125
and problem solving skills developed during

255
00:11:17.075 –> 00:11:20.045
that you developed during your research translated into your

256
00:11:20.045 –> 00:11:21.085
day-to-day role?

257
00:11:22.585 –> 00:11:25.205
Oh, yeah, definitely. I mean, yeah, so obviously,

258
00:11:25.205 –> 00:11:28.125
so in my current role, um, we run a range

259
00:11:28.125 –> 00:11:30.925
of different models, um, water resource models, um,

260
00:11:30.925 –> 00:11:33.525
similar kind of a series of models upon models with,

261
00:11:33.745 –> 00:11:36.285
and we have a huge range of data sets coming from a variety

262
00:11:36.305 –> 00:11:37.565
of different sources.

263
00:11:38.305 –> 00:11:43.005
Um, and this is all quite similar to, to related, relatable

264
00:11:43.065 –> 00:11:45.285
to the work I was doing in academia.

265
00:11:45.765 –> 00:11:48.725
I mean, I was working with huge geochemical databases

266
00:11:48.825 –> 00:11:51.765
or, um, big data sets that I was analyzing.

267
00:11:51.865 –> 00:11:54.645
So it’s kind of similar in that, in that way.

268
00:11:55.465 –> 00:11:59.885
Um, and yeah, so I guess, uh,

269
00:12:00.555 –> 00:12:02.885
yeah, we’re a, a lot, a lot of the, a lot of the problems

270
00:12:02.885 –> 00:12:05.005
that I faced in my academic career were quite similar

271
00:12:06.025 –> 00:12:08.325
to the ones I face in my day-to-day role, just kind

272
00:12:08.325 –> 00:12:11.165
of in a different field or subject matter.

273
00:12:12.465 –> 00:12:16.525
Um, and they, yeah, they’ve, they’ve developed in a lot

274
00:12:16.525 –> 00:12:21.135
of other ways in that I now, I now work as part of a,

275
00:12:21.375 –> 00:12:22.535
a team, I guess.

276
00:12:22.595 –> 00:12:26.855
So it’s, it’s actually, um, more

277
00:12:28.405 –> 00:12:30.375
solving problems, not just solely on your own.

278
00:12:30.525 –> 00:12:32.815
It’s actually a kind of a difficult, different,

279
00:12:33.175 –> 00:12:35.215
a different skill to solving a problem, kind of as,

280
00:12:35.215 –> 00:12:36.335
as a team, I would say.

281
00:12:36.395 –> 00:12:38.615
And do they have similar backgrounds in your team?

282
00:12:39.435 –> 00:12:40.495
Um, a range. Yeah.

283
00:12:40.635 –> 00:12:45.185
So I think I’d say over, over half have a pH PhD

284
00:12:45.445 –> 00:12:47.585
or have had postdoc experience.

285
00:12:48.005 –> 00:12:51.465
Um, but we also have, um, a couple who, who don’t come,

286
00:12:51.925 –> 00:12:53.185
um, from consultancy.

287
00:12:53.525 –> 00:12:56.225
So they kind of, they, they did like an environmental degree

288
00:12:56.225 –> 00:12:57.385
and they went into consultancy.

289
00:12:57.805 –> 00:12:59.425
So there’s, there’s a, there’s a big range

290
00:12:59.605 –> 00:13:03.345
and which actually is, I personally find brilliant to work

291
00:13:03.345 –> 00:13:04.665
with different people who all have

292
00:13:04.665 –> 00:13:05.785
a different kind of perspective.

293
00:13:06.585 –> 00:13:08.745
’cause you actually, you, the end results,

294
00:13:08.805 –> 00:13:11.025
you are generally far superior to

295
00:13:11.025 –> 00:13:13.105
what you would’ve achieved on your own. Um,

296
00:13:13.835 –> 00:13:14.835
Yeah. So do you, do

297
00:13:14.835 –> 00:13:16.885
you think, have you noticed a particular

298
00:13:17.435 –> 00:13:21.485
mindset or approach amongst the researchers

299
00:13:21.625 –> 00:13:23.165
or the people with the PhDs

300
00:13:23.165 –> 00:13:26.885
and the postdocs that gives them an edge maybe against the

301
00:13:26.885 –> 00:13:29.655
non-academic make people? Yeah.

302
00:13:29.925 –> 00:13:31.015
Yeah. De definitely.

303
00:13:31.095 –> 00:13:33.055
I mean, it’s, I guess that’s, you can kind of see that

304
00:13:33.635 –> 00:13:35.775
in our team that we, um, majority,

305
00:13:35.965 –> 00:13:38.775
most majority do have a PhD, um, or a master’s.

306
00:13:39.195 –> 00:13:41.535
Um, and I think it comes on, it boils down to a lot

307
00:13:41.535 –> 00:13:42.655
of different factors.

308
00:13:42.855 –> 00:13:45.935
I mean, one of them is being most PhD students

309
00:13:45.935 –> 00:13:49.835
with postdoctoral students, they’re generally self-starters.

310
00:13:49.935 –> 00:13:51.555
So they, they’re able to find a problem,

311
00:13:51.615 –> 00:13:55.035
solves have confidence to solve that problem independently.

312
00:13:55.065 –> 00:13:57.955
They don’t necessarily need their hand being held,

313
00:13:58.645 –> 00:14:01.875
which is really a really rare skill.

314
00:14:02.455 –> 00:14:04.075
Um, but it’s not just that,

315
00:14:04.075 –> 00:14:06.075
it’s also still having the wherewithal

316
00:14:06.775 –> 00:14:08.715
and con confidence to kind of touch base

317
00:14:08.945 –> 00:14:11.075
with like a supervisor when they get stuck.

318
00:14:11.575 –> 00:14:15.595
So that, that kind of process, i i, that kinda skill set

319
00:14:15.595 –> 00:14:18.875
and that process is a mindset, um, I guess

320
00:14:18.875 –> 00:14:20.075
of the perseverance

321
00:14:20.075 –> 00:14:23.355
and thinking for yourself, that’s quite like, difficult

322
00:14:23.355 –> 00:14:25.235
to find and it’s quite hard to teach.

323
00:14:25.895 –> 00:14:29.595
So I that I think it’s like a gem of a skillset that,

324
00:14:29.665 –> 00:14:31.395
that we love in our, in our team.

325
00:14:31.875 –> 00:14:33.635
I guess there’s a few of the things, I guess communication

326
00:14:33.635 –> 00:14:38.585
skills, um, are quite, being able

327
00:14:38.585 –> 00:14:41.465
to, which isn’t, it is not as common, I guess, in academia.

328
00:14:41.485 –> 00:14:45.585
But I guess the ability, if you’re able to communicate

329
00:14:45.585 –> 00:14:48.745
to anyone from all walks of life, not just technical people,

330
00:14:48.765 –> 00:14:51.495
that’s gonna give you a huge edge against,

331
00:14:52.395 –> 00:14:54.255
um, a lot, a lot of other people.

332
00:14:55.235 –> 00:14:59.575
Um, time management, which is quite co quite common

333
00:14:59.575 –> 00:15:01.655
with obviously postop PhDs and postdocs,

334
00:15:01.655 –> 00:15:05.575
because you often have only a shortterm short term funding,

335
00:15:05.675 –> 00:15:08.335
trying to get a bit of work done, trying

336
00:15:08.335 –> 00:15:11.015
to get it published, ready for the next,

337
00:15:11.195 –> 00:15:12.455
trying to find the next funding.

338
00:15:12.915 –> 00:15:15.175
Um, so that, that time management skill is crucial.

339
00:15:15.675 –> 00:15:19.735
Um, uh, in, in, we, we, we kind of love that,

340
00:15:19.735 –> 00:15:21.375
that skill set in, in the EA

341
00:15:21.375 –> 00:15:22.975
because it’s, it’s actually quite, it’s,

342
00:15:22.975 –> 00:15:25.055
it’s fairly similar, I guess, in, in government in

343
00:15:25.055 –> 00:15:28.255
that we quite often have short term funding projects.

344
00:15:28.835 –> 00:15:30.175
You might have a permanent role in government,

345
00:15:30.175 –> 00:15:32.135
but we’re, we’re often working on different projects.

346
00:15:32.555 –> 00:15:37.005
Um, so it’s kind of that similar, um, Yeah, that kind

347
00:15:37.005 –> 00:15:38.085
of similar skillset.

348
00:15:39.265 –> 00:15:42.295
Um, yeah, I think

349
00:15:42.345 –> 00:15:44.015
About project management. Is that

350
00:15:44.095 –> 00:15:45.095
A big one? Yeah, definitely.

351
00:15:45.095 –> 00:15:48.805
Um, yeah, the people who, those, those

352
00:15:48.825 –> 00:15:52.765
who those who succeed in industry are generally have, have

353
00:15:52.765 –> 00:15:54.645
to very, very organized with their time.

354
00:15:54.665 –> 00:15:58.445
So are able to calendar, block, hit internal

355
00:15:58.465 –> 00:15:59.565
and external deadlines

356
00:16:00.145 –> 00:16:02.885
and struggle, multiple expectations that are on them.

357
00:16:02.905 –> 00:16:06.005
So constantly reprioritizing deprioritizing

358
00:16:06.075 –> 00:16:07.965
work project, yeah.

359
00:16:07.965 –> 00:16:11.325
Project management, budget management is, is, yeah,

360
00:16:11.745 –> 00:16:14.125
really important skill set, especially depending on

361
00:16:14.125 –> 00:16:15.805
what grade you come in, in, out of the aea.

362
00:16:16.225 –> 00:16:20.325
Um, that would be, um, yeah, very, very valuable. Yeah.

363
00:16:20.325 –> 00:16:23.085
And I guess it, working on projects, um,

364
00:16:23.955 –> 00:16:28.015
multi-stakeholder projects are we, um,

365
00:16:29.035 –> 00:16:32.215
always very interested in people who, who have that, um,

366
00:16:32.215 –> 00:16:33.655
have had that experience in that.

367
00:16:34.435 –> 00:16:35.775
So, um, yeah,

368
00:16:35.775 –> 00:16:38.095
and kind of working with others to deliver something.

369
00:16:38.555 –> 00:16:41.815
Um, and again, it’s that juggling of priorities,

370
00:16:42.895 –> 00:16:45.015
managing different workloads, different priorities,

371
00:16:45.035 –> 00:16:47.575
and different needs from different partners.

372
00:16:48.275 –> 00:16:50.615
Um, so that kind of situation is very directly applicable

373
00:16:50.675 –> 00:16:53.335
to, to industry and towards the EA specifically.

374
00:16:54.075 –> 00:16:55.975
Um, so we really recognize that as a,

375
00:16:55.975 –> 00:16:57.255
as a really important skill set.

376
00:16:57.635 –> 00:16:58.895
Um, yeah.

377
00:17:00.385 –> 00:17:02.475
Yeah. Having that self-starter kind

378
00:17:02.475 –> 00:17:05.035
of take the initiative, get things done.

379
00:17:05.035 –> 00:17:07.595
Exactly. ’cause there is a lot of that in,

380
00:17:08.015 –> 00:17:10.315
in the ea especially if you’re working in a kinda

381
00:17:10.595 –> 00:17:11.635
national team from home.

382
00:17:12.095 –> 00:17:13.915
Mm-hmm. You’ve got to, it really,

383
00:17:13.915 –> 00:17:16.195
really gives you the edge when you’re coming in

384
00:17:16.195 –> 00:17:17.835
and working, doesn’t it? Yeah.

385
00:17:17.905 –> 00:17:20.675
It’s just not having the fear, basically. Yeah, no,

386
00:17:20.775 –> 00:17:21.775
You can do this. You’ve

387
00:17:21.775 –> 00:17:24.195
done it for, yeah. You’ve already been doing it.

388
00:17:24.755 –> 00:17:26.875
I just wonder if there’s any like, misconceptions,

389
00:17:27.535 –> 00:17:31.755
if you notice any misconceptions about working for the EA

390
00:17:32.505 –> 00:17:34.515
when you’ve come from Acade,

391
00:17:34.515 –> 00:17:37.235
when you came from academia. Is there anything you thought?

392
00:17:37.465 –> 00:17:40.755
Yeah, no, there’s, there’s quite a few actually that

393
00:17:41.305 –> 00:17:43.075
comp I was completely wrong about.

394
00:17:43.415 –> 00:17:46.755
So I think one of them was my, was my misconception.

395
00:17:47.035 –> 00:17:47.915
I guess Maria, it’s probably

396
00:17:47.915 –> 00:17:49.205
fairly common amongst academics.

397
00:17:49.285 –> 00:17:51.925
I would say that industry is easier than academia.

398
00:17:52.505 –> 00:17:54.405
Um, it’s a bit boring, a bit easy.

399
00:17:55.025 –> 00:17:56.245
Um, I would,

400
00:17:57.085 –> 00:17:59.445
I was very naive coming into working in the EA thinking

401
00:17:59.445 –> 00:18:02.285
that I think in reality there’s a, there’s a lot

402
00:18:02.285 –> 00:18:03.845
of very technical problems that need solving

403
00:18:03.845 –> 00:18:08.165
that aren’t reality, higher pressure projects,

404
00:18:09.105 –> 00:18:11.125
um, and the problems that require a lot

405
00:18:11.125 –> 00:18:13.045
of compromise in terms of time and resource.

406
00:18:13.045 –> 00:18:15.485
And they’re generally higher pressure projects,

407
00:18:16.065 –> 00:18:18.205
so they involve like very difficult decisions and policy.

408
00:18:18.355 –> 00:18:21.525
They impact, they impact millions of people, billions

409
00:18:21.525 –> 00:18:22.565
of pounds of taxpayer money.

410
00:18:23.025 –> 00:18:25.645
So in reality, I’d say that my job now is much more

411
00:18:26.205 –> 00:18:28.685
stressful than my academic job

412
00:18:28.685 –> 00:18:31.005
because it’s the stake so much higher ultimately.

413
00:18:31.705 –> 00:18:33.765
Um, so that’s one of them.

414
00:18:34.195 –> 00:18:37.765
There’s a bit a misconception that I had that, um,

415
00:18:38.345 –> 00:18:41.005
by leaving academia, like, you’re closing that door

416
00:18:41.425 –> 00:18:42.725
and that you’re, you can’t publish

417
00:18:42.825 –> 00:18:45.125
or be involved in research in industry.

418
00:18:45.145 –> 00:18:47.085
So I think, I mean, since being in the ea my team has

419
00:18:47.085 –> 00:18:48.205
published two papers.

420
00:18:49.225 –> 00:18:51.685
Um, we’ve, we work alongside academics daily.

421
00:18:52.545 –> 00:18:55.365
Um, so that was completely, I think it,

422
00:18:55.365 –> 00:18:56.485
obviously it’s very dependent on

423
00:18:56.485 –> 00:18:58.165
what role you get in your organization

424
00:18:58.165 –> 00:19:00.125
that won’t be the same forever for, for

425
00:19:00.855 –> 00:19:03.605
every role in the ea, but they do exist.

426
00:19:03.745 –> 00:19:05.245
That’s what I’m, I’m trying to say. Mm.

427
00:19:05.865 –> 00:19:07.565
Um, I had a couple of other ones.

428
00:19:07.565 –> 00:19:10.725
I guess like one, I think the, the idea

429
00:19:10.725 –> 00:19:14.245
that industry would just be like a nine till five kind

430
00:19:14.245 –> 00:19:16.565
of job, and it obviously can be,

431
00:19:16.665 –> 00:19:18.405
but it can also be quite flexible.

432
00:19:19.005 –> 00:19:21.725
I mean, um, I think it’s a lot of it’s to do with

433
00:19:21.975 –> 00:19:23.085
after covid as well.

434
00:19:23.585 –> 00:19:26.045
Um, so I actually work majority from home.

435
00:19:26.385 –> 00:19:28.525
Um, so I work in a national team, which I kind of allows

436
00:19:28.525 –> 00:19:29.765
that, but I have a lot

437
00:19:29.765 –> 00:19:31.365
of flexibility in how I work my hours.

438
00:19:31.465 –> 00:19:33.245
So I didn’t actually, and I was my, one

439
00:19:33.245 –> 00:19:35.205
of my fears moving from academia to industries,

440
00:19:35.245 –> 00:19:36.325
I would lose that flexibility.

441
00:19:36.745 –> 00:19:37.925
And I, and I haven’t at all.

442
00:19:38.745 –> 00:19:43.325
Um, and I guess like this kind of related to like the, um,

443
00:19:43.725 –> 00:19:46.005
industry being easy that the misconception that

444
00:19:46.765 –> 00:19:48.485
industry is kind of be boring

445
00:19:48.505 –> 00:19:50.245
or less cutting edge or less innovative.

446
00:19:51.385 –> 00:19:54.085
Um, and I would, I would say

447
00:19:54.085 –> 00:19:55.325
that mean obviously you’re never gonna be,

448
00:19:55.325 –> 00:19:57.005
it’s never gonna be as cutting edge

449
00:19:57.005 –> 00:19:58.805
or innovative as, as research,

450
00:19:59.545 –> 00:20:03.045
but we do still work with academics very closely,

451
00:20:03.105 –> 00:20:05.685
and we’re constantly trying to bring in new ideas,

452
00:20:05.835 –> 00:20:07.765
data sets, models into our work in

453
00:20:07.765 –> 00:20:08.965
particular, like in particular.

454
00:20:08.965 –> 00:20:11.165
So for example, we, we work very closely with University

455
00:20:11.165 –> 00:20:14.805
of Bristol, Newcastle, east Anglia on things like hydrology,

456
00:20:14.855 –> 00:20:16.485
water resources, modeling, climate change.

457
00:20:17.625 –> 00:20:21.365
Um, so we do, it’s, I wouldn’t say we’re less cutting edge

458
00:20:21.365 –> 00:20:23.925
or less innovative, if anything we’re, we’re trying

459
00:20:23.925 –> 00:20:27.885
to actively bring all those pieces in into government

460
00:20:27.945 –> 00:20:31.205
to actually, um, make, make sure that those

461
00:20:32.095 –> 00:20:33.965
great innovations are actually having an impact

462
00:20:34.185 –> 00:20:36.085
on people’s daily lives. So

463
00:20:36.115 –> 00:20:37.115
Yeah.

464
00:20:37.365 –> 00:20:38.925
I would also say one of the misconceptions

465
00:20:38.925 –> 00:20:41.885
with the e the EA is that you might have heard

466
00:20:41.885 –> 00:20:43.045
of one part of the ea.

467
00:20:43.225 –> 00:20:45.525
So if I ever mentioned to someone, oh,

468
00:20:45.525 –> 00:20:48.445
where can the ea they’ll go, well, flooding in my area.

469
00:20:48.925 –> 00:20:52.765
Sure. I don’t, there’s like 12,000, 13,000 people.

470
00:20:52.825 –> 00:20:54.005
We work across England.

471
00:20:54.225 –> 00:20:56.965
We have area teams, we have national teams, we have

472
00:20:57.715 –> 00:21:00.365
very scientific research teams.

473
00:21:00.465 –> 00:21:01.725
We have the chief scientist group,

474
00:21:01.745 –> 00:21:05.605
we have modeling te like we have so much variation,

475
00:21:06.665 –> 00:21:09.445
um, agriculture, you name it, we have it.

476
00:21:09.985 –> 00:21:12.365
And it’s only even with us.

477
00:21:12.425 –> 00:21:15.205
We don’t know will somebody will pop it

478
00:21:15.345 –> 00:21:16.525
up and start talking to us.

479
00:21:16.525 –> 00:21:18.845
We like, I’ve never heard of that team. You do what?

480
00:21:19.305 –> 00:21:22.845
So honestly, we don’t even know the extent of the work, some

481
00:21:22.845 –> 00:21:23.845
of the work that we do.

482
00:21:24.505 –> 00:21:27.125
So it’s very, it’s so very vari varied.

483
00:21:27.225 –> 00:21:30.925
Um, so yeah, I just wanted to put that out there. No,

484
00:21:30.925 –> 00:21:32.525
Completely agree. It’s a really good one.

485
00:21:33.505 –> 00:21:36.125
Um, but just thinking of the practicalities

486
00:21:36.125 –> 00:21:38.885
of a career in the, in the environment agency, um,

487
00:21:40.075 –> 00:21:41.965
what does your typical day

488
00:21:42.265 –> 00:21:45.245
or a project look like in your current role?

489
00:21:45.845 –> 00:21:48.005
Hmm. I mean, it, yeah, it can, it can vary.

490
00:21:48.005 –> 00:21:49.325
It’ll vary a lot. I think, to be honest,

491
00:21:49.395 –> 00:21:51.205
it’s quite nice at the moment because it’s

492
00:21:51.205 –> 00:21:52.565
after the Easter break, it’s all very quiet

493
00:21:53.165 –> 00:21:55.805
because most people are on half term or, um, on holiday.

494
00:21:55.825 –> 00:21:57.965
So it’s nice. Um, but not, that isn’t, that’s not normal.

495
00:21:58.145 –> 00:22:01.245
Um, most of the time. Uh, I guess typical day,

496
00:22:01.325 –> 00:22:02.685
a multiple meetings a day.

497
00:22:03.065 –> 00:22:04.525
So I have, I’m, I line manage.

498
00:22:04.545 –> 00:22:07.605
So I have meetings with my linee, often have meetings

499
00:22:07.605 –> 00:22:11.605
with a project team, project partners, consultants,

500
00:22:11.605 –> 00:22:13.485
obviously I said we’re running consultancy projects

501
00:22:13.485 –> 00:22:15.605
and meeting with consultants and getting updates

502
00:22:15.665 –> 00:22:18.245
and solving problems kind of as, as we go.

503
00:22:19.785 –> 00:22:21.885
Um, see a lot of time those kind

504
00:22:21.885 –> 00:22:23.605
of meetings are discussing kind of best ways

505
00:22:23.625 –> 00:22:26.405
to solve data problems or technical problems.

506
00:22:27.305 –> 00:22:31.125
Um, or I mean, and a, a chunk of that is project management.

507
00:22:31.425 –> 00:22:33.845
Um, so communicating priorities, deadlines, delays,

508
00:22:33.955 –> 00:22:36.725
what a deliverables gonna look like, those kind of things.

509
00:22:37.105 –> 00:22:40.325
But when I’m not in meetings, which is lovely, um,

510
00:22:40.425 –> 00:22:42.845
I’m writing, so things like technical reports,

511
00:22:43.275 –> 00:22:46.965
currently writing a technical report, creating slide packs

512
00:22:47.545 –> 00:22:50.405
of, I guess the work that we’re doing, providing results

513
00:22:50.625 –> 00:22:54.365
to different project partners, um, developing

514
00:22:55.145 –> 00:22:56.445
and updating models.

515
00:22:56.825 –> 00:22:59.965
So I do, I don’t get to do it as much in my current role,

516
00:22:59.985 –> 00:23:03.285
but in my previous role, I was actually,

517
00:23:03.405 –> 00:23:05.485
I was actively programming most of the day,

518
00:23:05.485 –> 00:23:09.085
so it was coding, um, and working, doing data analysis

519
00:23:09.085 –> 00:23:11.285
and interpreting lots of results from our models.

520
00:23:11.945 –> 00:23:14.925
So yeah, we do a lot of the developing updating models,

521
00:23:15.965 –> 00:23:19.365
bringing in data, creating a lot of different data products

522
00:23:19.365 –> 00:23:22.765
that are shared with partners, that’s data sets, dashboards

523
00:23:22.765 –> 00:23:25.005
to share things, creating maps.

524
00:23:25.745 –> 00:23:28.685
Um, and then a lot, a lot of it’s presenting,

525
00:23:28.745 –> 00:23:31.005
so presenting results to different audiences.

526
00:23:31.005 –> 00:23:34.125
So I, I present to government technical experts,

527
00:23:34.525 –> 00:23:35.845
academics, water companies.

528
00:23:35.845 –> 00:23:37.725
We work with a lot consultants.

529
00:23:37.785 –> 00:23:41.305
So I mean, also, it’s actually quite similar to

530
00:23:41.305 –> 00:23:44.585
what I was doing in my academic day to day, like as in

531
00:23:45.525 –> 00:23:48.125
analysis of data, running models, running lab,

532
00:23:48.525 –> 00:23:49.605
I was running lab experiments.

533
00:23:49.605 –> 00:23:51.365
It’s a bit slightly different, but running,

534
00:23:51.365 –> 00:23:52.925
running scenarios.

535
00:23:53.385 –> 00:23:57.405
Um, interpreting results. Presenting results.

536
00:23:58.175 –> 00:24:00.085
Again, it’s all, yeah, it’s all very similar to I was doing

537
00:24:00.745 –> 00:24:02.805
in my postdoc. Yeah.

538
00:24:03.435 –> 00:24:05.365
Fantastic. Just thinking about, um,

539
00:24:05.385 –> 00:24:06.565
the postdocs on the call

540
00:24:07.145 –> 00:24:09.295
and if they were looking at, um,

541
00:24:09.575 –> 00:24:13.735
a career in the environment agency, what might, um,

542
00:24:13.965 –> 00:24:15.575
help ’em stand out in the hiring process

543
00:24:15.875 –> 00:24:18.015
or any common mistakes

544
00:24:18.085 –> 00:24:21.495
that postdocs might make when applying for academic roles?

545
00:24:22.335 –> 00:24:24.415
So, two sides to that, really.

546
00:24:24.685 –> 00:24:27.315
Yeah. Um, yeah, I’ll do the,

547
00:24:27.415 –> 00:24:29.635
how could they stand out first, I guess?

548
00:24:30.655 –> 00:24:34.365
Um, first, okay.

549
00:24:34.425 –> 00:24:37.205
In the EA we have kinda an application interview process,

550
00:24:37.695 –> 00:24:39.645
which is very much competency

551
00:24:39.745 –> 00:24:43.685
and capability based, style based, um,

552
00:24:44.675 –> 00:24:47.185
which can be, it’s, it’s fairly common in industry,

553
00:24:47.205 –> 00:24:50.105
but ultimately you’re gonna have to, it is a little bit

554
00:24:51.095 –> 00:24:52.215
tedious sometimes,

555
00:24:52.315 –> 00:24:54.535
but I think do you have to kind of get on board

556
00:24:54.535 –> 00:24:55.935
with it if you wanna come into industry

557
00:24:55.935 –> 00:24:58.615
and if you want to get, to get a role in

558
00:24:58.615 –> 00:24:59.855
the EA specifically.

559
00:25:00.435 –> 00:25:03.115
Um, so I, I would say do put time

560
00:25:03.115 –> 00:25:05.535
and effort into that, into that.

561
00:25:06.035 –> 00:25:08.735
Um, ’cause it does it by the time you get to interview,

562
00:25:08.735 –> 00:25:09.895
it’s really obvious if you haven’t prepped.

563
00:25:10.755 –> 00:25:14.995
Um, and it, it does, it does, it does, um, count for a lot,

564
00:25:15.615 –> 00:25:17.595
um, ways to stand out.

565
00:25:17.955 –> 00:25:19.755
I think as, I mean, postdocs are always,

566
00:25:20.855 –> 00:25:22.375
I want postdocs I’ve interviewed

567
00:25:22.375 –> 00:25:24.055
and PhD students I’ve interviewed.

568
00:25:24.995 –> 00:25:26.255
The technical is never the problem.

569
00:25:27.195 –> 00:25:28.975
We know that you’re brilliant at the technical,

570
00:25:29.245 –> 00:25:32.375
it’s interpersonal softer skills that are often

571
00:25:32.885 –> 00:25:34.655
sometimes go a bit missing.

572
00:25:34.915 –> 00:25:37.695
So I think don’t, don’t underestimate

573
00:25:37.775 –> 00:25:39.815
or undervalue those skills.

574
00:25:40.435 –> 00:25:43.775
We care equally about your ability to communicate, um,

575
00:25:44.075 –> 00:25:46.255
or your approaches to working as a team or how your project

576
00:25:46.275 –> 00:25:49.335
and time manage as we care about your technical skills.

577
00:25:49.715 –> 00:25:51.415
So don’t just focus too much on the technical.

578
00:25:52.115 –> 00:25:54.695
Um, I think one big thing,

579
00:25:55.515 –> 00:25:58.615
unless you’re applying for a role

580
00:25:58.615 –> 00:26:02.375
that’s basically the exact same as your postdoc role

581
00:26:02.835 –> 00:26:05.455
or project, don’t assume that the, the app,

582
00:26:05.515 –> 00:26:08.295
the interview panel, know what you’re talking about.

583
00:26:09.115 –> 00:26:11.135
Um, make it really app applicable

584
00:26:11.135 –> 00:26:12.895
and relevant to the role that you’re applying for.

585
00:26:13.045 –> 00:26:15.455
Because a lot of, and,

586
00:26:15.475 –> 00:26:18.255
and tailor your communication according accordingly.

587
00:26:18.875 –> 00:26:20.775
Um, I think, oh yeah.

588
00:26:20.835 –> 00:26:22.335
One of the things I’d say is also, I mean,

589
00:26:22.495 –> 00:26:24.415
although all, a lot of the candidates have done really well

590
00:26:24.955 –> 00:26:29.825
in our recent round, um, I think, uh, make your,

591
00:26:30.375 –> 00:26:34.145
make your application just make be be more human personable.

592
00:26:34.485 –> 00:26:35.745
We want to know about you as a person,

593
00:26:36.125 –> 00:26:37.905
so why you’re interested in the role

594
00:26:37.905 –> 00:26:38.945
that you’re applying for.

595
00:26:39.245 –> 00:26:40.465
Are you passionate about it?

596
00:26:41.175 –> 00:26:42.545
Even if it’s different to your past career.

597
00:26:43.385 –> 00:26:44.445
We, we, we might be,

598
00:26:44.625 –> 00:26:46.325
we probably don’t care, but why the change?

599
00:26:46.325 –> 00:26:48.165
What interests you about it?

600
00:26:48.985 –> 00:26:51.165
Um, so I think, yeah, do a lot, do a bit of reading

601
00:26:51.505 –> 00:26:54.005
around the role, the sector, the industry

602
00:26:54.005 –> 00:26:55.165
that you’re applying for

603
00:26:55.305 –> 00:26:57.325
and have some kind of questions prepared.

604
00:26:58.065 –> 00:27:01.605
Um, and mostly just to kind of show your, your knowledge

605
00:27:01.725 –> 00:27:04.045
or interest in the kind of landscape of the,

606
00:27:04.145 –> 00:27:05.605
the role you’re applying for.

607
00:27:06.065 –> 00:27:08.885
Um, I mean, one thing that really stood out to me

608
00:27:09.685 –> 00:27:12.245
recently was people who reach out as well.

609
00:27:12.545 –> 00:27:16.605
Um, so ask questions, ring, ring up the recruitment manager,

610
00:27:16.945 –> 00:27:19.765
ask questions about the job and the role.

611
00:27:20.215 –> 00:27:22.125
Quite often, ea job apps are a little bit

612
00:27:22.175 –> 00:27:23.765
vague, a little bit minimal.

613
00:27:24.265 –> 00:27:28.085
Um, so there’s quite often quite a lot of industry jargon.

614
00:27:28.185 –> 00:27:32.405
So don’t, don’t worry, call up, ask for info.

615
00:27:32.705 –> 00:27:35.125
We, I know I personally love it.

616
00:27:35.465 –> 00:27:36.725
So actually get to know people a bit more

617
00:27:36.725 –> 00:27:38.925
before they apply our interview.

618
00:27:39.985 –> 00:27:41.965
Um, and that kind of mean, kind of

619
00:27:42.875 –> 00:27:44.245
relates to common mistakes.

620
00:27:44.305 –> 00:27:46.445
So yeah, if I think not reaching out,

621
00:27:47.595 –> 00:27:49.085
it’s probably a bit of a common mistake.

622
00:27:49.085 –> 00:27:52.205
So that often in, in the ad, uh, like the, the name

623
00:27:52.305 –> 00:27:54.325
and the phone number will be there, why not use it?

624
00:27:54.985 –> 00:27:58.965
Um, and I think, yeah, I kind of, I kind of mentioned

625
00:27:58.965 –> 00:28:02.605
that in terms of mistakes of that in, in terms of, um,

626
00:28:03.165 –> 00:28:07.685
assuming that we will understand what your research is

627
00:28:08.105 –> 00:28:13.005
or does, um, we, like, I I we need to kind of,

628
00:28:13.005 –> 00:28:14.925
I think you need to quite clear on like the objectives

629
00:28:14.985 –> 00:28:17.645
of the project, the role, why does your research matter?

630
00:28:18.195 –> 00:28:19.845
What did you achieve? And try

631
00:28:19.845 –> 00:28:23.165
and, I guess get, get to the point a little bit.

632
00:28:23.165 –> 00:28:26.005
Yeah. Demonstrate that skill that they’re asking for,

633
00:28:26.005 –> 00:28:27.045
whether it’s communication

634
00:28:27.105 –> 00:28:29.445
or achieve results, like how have you done it?

635
00:28:29.505 –> 00:28:30.845
Not just what you actually did,

636
00:28:31.235 –> 00:28:34.885
because we see so much of that I did this then this then,

637
00:28:34.945 –> 00:28:38.605
and you’re not actually explaining yourself as to how

638
00:28:38.665 –> 00:28:39.885
and, and why you’ve done it.

639
00:28:40.355 –> 00:28:44.445
It’s, it’s so true. Like I quite often in, in,

640
00:28:44.465 –> 00:28:47.325
in interviews like you get, you can get into the minutia

641
00:28:47.585 –> 00:28:50.605
of detail around someone’s research topic and, and,

642
00:28:50.705 –> 00:28:52.245
and quite often it is interesting,

643
00:28:52.265 –> 00:28:54.005
but your time limited in interviews.

644
00:28:54.025 –> 00:28:57.085
So you really need to make, get, get to the point about

645
00:28:57.115 –> 00:28:59.005
what, what, what skillset are you demonstrating

646
00:28:59.345 –> 00:29:01.645
by presenting this experience?

647
00:29:01.785 –> 00:29:04.005
Or what knowledge are you presenting

648
00:29:04.035 –> 00:29:05.085
when you’re talking about it?

649
00:29:05.115 –> 00:29:07.605
Kind of, um, really show how,

650
00:29:07.665 –> 00:29:11.565
and I think even, even, even if it’s not directly applicable

651
00:29:11.645 –> 00:29:13.765
to that sector, how could it be relevant to the role?

652
00:29:13.765 –> 00:29:16.805
Mm-hmm. Try that’s key. Yeah. Right. Bring it out

653
00:29:16.825 –> 00:29:18.005
The end. Yeah, bring it back. Bring it

654
00:29:18.005 –> 00:29:18.765
Back round. And this

655
00:29:18.765 –> 00:29:20.285
Is how I’d use the skills in this role.

656
00:29:20.635 –> 00:29:22.805
Exactly. Or I’d take it forward in this role.

657
00:29:22.995 –> 00:29:24.365
Yeah, definitely. Mm-hmm.

658
00:29:24.945 –> 00:29:28.005
So how, what, before we move on to the, um, questions,

659
00:29:28.475 –> 00:29:31.965
what grade level should a PhD student postdocs

660
00:29:31.965 –> 00:29:33.245
be applying for, do you think?

661
00:29:33.825 –> 00:29:37.045
Oh, yeah. Um, that’s a, it’s a tricky one.

662
00:29:37.455 –> 00:29:39.925
Maybe if I just quickly cover the grade structure in the EA

663
00:29:40.285 –> 00:29:41.965
actually, because it’s a, a bit of a minefield in itself.

664
00:29:41.965 –> 00:29:45.365
Mm. Um, so generally have standard grades,

665
00:29:45.395 –> 00:29:46.485
kind of one to seven.

666
00:29:46.625 –> 00:29:49.045
We also have, but we also have things like graduate trainee

667
00:29:49.045 –> 00:29:51.565
grades and exec manager grades amongst other things.

668
00:29:51.705 –> 00:29:53.045
But standard grades are the most

669
00:29:53.045 –> 00:29:54.325
common in the environment agency.

670
00:29:54.785 –> 00:29:58.125
And if I kind of cover off probably realistically grade

671
00:29:58.175 –> 00:29:59.805
three to seven, which is the

672
00:29:59.805 –> 00:30:01.805
most relevant here, grade three maybe.

673
00:30:01.975 –> 00:30:05.725
We’ll see. So I guess grade three is kind of not really a,

674
00:30:05.905 –> 00:30:08.885
um, I’d say quite, quite young

675
00:30:08.945 –> 00:30:12.365
and probably would be more like undergraduate,

676
00:30:12.365 –> 00:30:13.885
masters, maybe level.

677
00:30:14.515 –> 00:30:17.645
Yeah. Um, grade four office, that’s kind

678
00:30:17.645 –> 00:30:19.005
of like an officer analyst.

679
00:30:19.265 –> 00:30:21.645
So they kind of support on technical specialist work.

680
00:30:21.835 –> 00:30:23.005
They contribute to projects.

681
00:30:23.375 –> 00:30:25.165
Grade five is more of a specialist

682
00:30:25.505 –> 00:30:27.965
or like a, a, a team leader for a small team.

683
00:30:28.785 –> 00:30:30.565
Um, grade six is a senior

684
00:30:30.565 –> 00:30:32.045
specialist or a senior team leader.

685
00:30:32.045 –> 00:30:34.685
So they generally kind of manage specialist teams

686
00:30:35.585 –> 00:30:38.885
or they contribute as a technical expert to the business.

687
00:30:39.025 –> 00:30:41.485
And then you have grade sevens, those are lead experts

688
00:30:41.705 –> 00:30:43.285
and they generally, um,

689
00:30:43.345 –> 00:30:44.725
or a manager, they’re kind of accountable

690
00:30:44.725 –> 00:30:46.965
for delivery in a specific area.

691
00:30:47.145 –> 00:30:48.365
So they typically lead like a lead

692
00:30:48.365 –> 00:30:49.725
or manage a large team or department.

693
00:30:49.885 –> 00:30:53.325
Hmm. So I think in terms of where you’d come in at h

694
00:30:53.385 –> 00:30:55.605
or postdoc, it really depends on the experience

695
00:30:55.745 –> 00:30:59.165
and how you are of how you fit for the specific role

696
00:30:59.165 –> 00:31:00.645
that you are applying for.

697
00:31:01.245 –> 00:31:03.685
I can only really speak to the experience in our modeling

698
00:31:03.715 –> 00:31:05.085
team, which is fairly technical,

699
00:31:05.705 –> 00:31:09.245
but across our department we’ve had PhD students come in at

700
00:31:09.245 –> 00:31:10.645
grade three, four, and five.

701
00:31:11.025 –> 00:31:14.085
It all kind of related, it kind of depends on the role.

702
00:31:14.385 –> 00:31:15.965
So those who’ve come in at a grade five,

703
00:31:16.395 –> 00:31:18.485
they generally have a very specific skillset.

704
00:31:19.505 –> 00:31:23.205
Um, or they’ve worked in kind of the same field that, um,

705
00:31:23.205 –> 00:31:25.205
and the skillset that, and the skillset required

706
00:31:25.205 –> 00:31:26.525
by the business, they have direct kinda

707
00:31:26.525 –> 00:31:27.685
experience in that role already.

708
00:31:28.585 –> 00:31:33.405
Um, so whereas postdocs so generally come in at a grade four

709
00:31:33.425 –> 00:31:36.685
or five, but again, it just dependent on the role.

710
00:31:37.385 –> 00:31:39.805
Um, so personally I came in at a grade four,

711
00:31:39.865 –> 00:31:41.365
but I think real realistically,

712
00:31:41.405 –> 00:31:42.805
I probably should have come in at grade five.

713
00:31:42.845 –> 00:31:44.445
I think I undersold myself a little bit.

714
00:31:44.985 –> 00:31:48.445
Um, and I, I was only really in that role for six months

715
00:31:48.445 –> 00:31:51.805
before I moved up to a grade five, which is that, um,

716
00:31:51.975 –> 00:31:53.685
kinda specialist role.

717
00:31:54.305 –> 00:31:57.805
Um, but there’s, I would say that in the EA there’s a lot

718
00:31:57.805 –> 00:32:00.525
of, just as a kind of tangent, there’s a lot of, um,

719
00:32:00.635 –> 00:32:03.045
opportunities to move around and move up.

720
00:32:03.945 –> 00:32:06.165
Um, although they might always be in the same kind of part

721
00:32:06.165 –> 00:32:09.565
of the part of business, you can, you can move around.

722
00:32:10.465 –> 00:32:11.845
Um, yeah.

723
00:32:11.845 –> 00:32:13.165
And then, yes, I’d say grades

724
00:32:13.965 –> 00:32:17.955
postdoc coming in at grade six is probably, could be done,

725
00:32:17.955 –> 00:32:20.915
but it, it does require a lot more experience.

726
00:32:20.915 –> 00:32:23.555
Maybe if you’ve had a lot more postdoctoral experience

727
00:32:23.735 –> 00:32:26.995
and perhaps running like a, a running a small research team

728
00:32:27.415 –> 00:32:31.655
for example, or, um, yeah, kind

729
00:32:31.655 –> 00:32:35.135
of running multiple projects for a re for a research team.

730
00:32:35.195 –> 00:32:37.535
You could probably come in at grade six then.

731
00:32:37.795 –> 00:32:38.975
But I’d say it really, it’s really,

732
00:32:39.020 –> 00:32:40.445
really dependent on the role that you’re applying for

733
00:32:40.445 –> 00:32:43.845
and how much experience you have have in, in that I’d

734
00:32:43.845 –> 00:32:44.845
Say. And that’s when

735
00:32:44.845 –> 00:32:46.485
it’s worth reaching out. Exactly.

736
00:32:46.505 –> 00:32:47.925
And speaking to the recruiting manager

737
00:32:48.025 –> 00:32:51.685
and see really, like, like Emily said,

738
00:32:52.145 –> 00:32:54.365
we are very restricted on our word count when we

739
00:32:54.385 –> 00:32:55.565
put in job adverts.

740
00:32:56.105 –> 00:32:58.085
So sometimes you read them

741
00:32:58.085 –> 00:33:01.245
and they’re a bit ambiguous, less so when they’re kind

742
00:33:01.245 –> 00:33:04.485
of scientific, but it’s always worth finding out

743
00:33:04.645 –> 00:33:06.405
’cause you’ve got that edge and you can tailor your

744
00:33:06.495 –> 00:33:10.405
competency answers and your job application to that role.

745
00:33:10.825 –> 00:33:13.125
And then if you get through what we call the blind shift,

746
00:33:13.285 –> 00:33:14.485
’cause we can’t see who’s coming through,

747
00:33:14.755 –> 00:33:17.365
then you can tailor your interview to that role

748
00:33:17.425 –> 00:33:18.765
as well, more so.

749
00:33:18.905 –> 00:33:20.405
But, um, it is really key

750
00:33:20.525 –> 00:33:22.445
’cause they’re all so completely different.

751
00:33:23.025 –> 00:33:26.245
Um, you just don’t know from one, one team to the next.

752
00:33:26.745 –> 00:33:29.005
So they might be looking for completely different skills

753
00:33:29.185 –> 00:33:30.285
and, and everything.

754
00:33:30.345 –> 00:33:31.345
So yeah,

755
00:33:32.725 –> 00:33:33.725
I couldn’t agree more.

756
00:33:34.825 –> 00:33:36.405
Um, I’ll just check in with Kerry.

757
00:33:36.985 –> 00:33:40.085
Um, are you, do you want, is it time, do you want us to go

758
00:33:40.105 –> 00:33:41.165
to questions or

759
00:33:41.435 –> 00:33:43.485
Sean, I think we’re ready for questions.

760
00:33:43.745 –> 00:33:45.165
And if I can suggest, just

761
00:33:45.165 –> 00:33:47.925
because it’s one that was also pre-submitted quite

762
00:33:48.205 –> 00:33:50.445
frequently that we start with Rory’s question, which is

763
00:33:50.445 –> 00:33:51.925
how common is it for art

764
00:33:51.985 –> 00:33:54.525
and humanities researchers to work in the ea

765
00:33:55.025 –> 00:33:57.565
and what sorts of roles do they tend to have?

766
00:33:59.455 –> 00:34:01.745
Yeah, I can, I can speak on this a little bit.

767
00:34:01.825 –> 00:34:02.865
I can’t speak on it fully as,

768
00:34:02.865 –> 00:34:05.465
obviously I come from quite a sciencey background,

769
00:34:06.205 –> 00:34:10.775
but, um, I’ve, I’ve, I’ve heard of I postdocs

770
00:34:10.775 –> 00:34:11.775
and PhDs who come from

771
00:34:11.775 –> 00:34:14.615
that more arts humanities background coming into the ea.

772
00:34:15.355 –> 00:34:17.975
Um, like I think again, obviously with the EA

773
00:34:18.515 –> 00:34:20.775
we have a strong focus on science, so geography,

774
00:34:20.885 –> 00:34:21.895
geology, environment.

775
00:34:22.035 –> 00:34:24.815
Um, so it’s, it’s probably more common.

776
00:34:25.915 –> 00:34:27.575
Um, but I guess this between the skillset that you’re

777
00:34:28.095 –> 00:34:30.135
acquired by doing any PhD or postdoc is still

778
00:34:30.455 –> 00:34:31.575
valuable to the ea.

779
00:34:31.795 –> 00:34:34.215
So I think they’re probably even more relevant to roles

780
00:34:34.355 –> 00:34:37.015
as perhaps more team leaders or project management roles

781
00:34:37.195 –> 00:34:39.775
and obviously technical specialist roles that we can offer.

782
00:34:40.635 –> 00:34:43.585
Um, again, I, as I I should say as well,

783
00:34:43.585 –> 00:34:45.505
I mean we’ve mentioned it before, the EA recruit based on

784
00:34:45.505 –> 00:34:46.585
competencies and capabilities.

785
00:34:46.685 –> 00:34:48.265
So that’s kind of based on your potential

786
00:34:48.365 –> 00:34:50.665
and ability to learn or adapt

787
00:34:50.665 –> 00:34:54.465
or transfer your skill, um, to a different topic.

788
00:34:54.605 –> 00:34:57.905
So if you can show that you can apply all the skills

789
00:34:57.905 –> 00:34:59.145
that you’ve learned to that role

790
00:34:59.885 –> 00:35:01.745
and you can make it relevant to the role you’re applying

791
00:35:01.765 –> 00:35:03.065
for, you’ll, you’ll stand a chance.

792
00:35:03.225 –> 00:35:06.625
I think you will struggle with achieving a, a,

793
00:35:06.625 –> 00:35:07.705
like a technical spec,

794
00:35:07.985 –> 00:35:10.145
a tech spec role at a grade five or six.

795
00:35:10.205 –> 00:35:12.465
So probably more realistically coming in it

796
00:35:13.185 –> 00:35:15.025
a good grade four perhaps.

797
00:35:15.175 –> 00:35:17.185
Yeah. Um, but um,

798
00:35:17.285 –> 00:35:18.945
but there, there’ll be parts of the business that

799
00:35:19.445 –> 00:35:21.585
to be more suitable for Yeah.

800
00:35:21.585 –> 00:35:24.985
Things like project management roles, um, maybe more kind

801
00:35:24.985 –> 00:35:26.345
of people, people roles.

802
00:35:26.345 –> 00:35:27.385
You have HR roles,

803
00:35:27.425 –> 00:35:30.665
So a lot of people, uh, there’s a, a big team of, uh,

804
00:35:31.285 –> 00:35:32.785
the people program mm-hmm.

805
00:35:32.785 –> 00:35:33.945
That look after recruitment.

806
00:35:34.295 –> 00:35:36.585
Even my team, my background is science,

807
00:35:36.685 –> 00:35:41.125
but we’ve got a, his, uh, person that studied his history

808
00:35:41.545 –> 00:35:43.645
and they’ve, and we are looking at a grade four at the

809
00:35:43.645 –> 00:35:44.885
moment, training skills.

810
00:35:44.905 –> 00:35:47.045
And I’ve seen people with PhDs come through.

811
00:35:47.745 –> 00:35:50.085
Um, and yeah, we, we look at them,

812
00:35:50.315 –> 00:35:52.205
they probably wouldn’t be looking at the grade five

813
00:35:52.365 –> 00:35:55.005
’cause then, ’cause you need a certain level of experience,

814
00:35:55.345 –> 00:35:57.565
two or three years training and stuff.

815
00:35:57.585 –> 00:35:59.205
But it just, it depends on the role.

816
00:35:59.665 –> 00:36:02.165
But it definitely, we have quite a lot of those kind

817
00:36:02.165 –> 00:36:04.045
of roles in the EA as well.

818
00:36:04.475 –> 00:36:07.125
Obviously not as many as the, the scientific ones,

819
00:36:07.125 –> 00:36:09.165
but we do definitely have quite a,

820
00:36:09.245 –> 00:36:11.325
a large proportion. Mm-hmm.

821
00:36:12.285 –> 00:36:13.805
I think that’s where, probably where it comes to as well,

822
00:36:13.945 –> 00:36:17.965
if you, if you see something pop up on our website, again,

823
00:36:17.965 –> 00:36:20.205
reach out to the recruitment manager, talk

824
00:36:20.205 –> 00:36:23.285
to ’em about your experience and, and your skillset and,

825
00:36:23.585 –> 00:36:25.925
and go from there really and see, see what they think

826
00:36:25.925 –> 00:36:28.845
and if you’d be a suitable candidate to, to apply.

827
00:36:29.395 –> 00:36:32.045
Yeah. I think that’s one of the big things about the EA

828
00:36:32.145 –> 00:36:35.045
and everyone I speak to is that they’re,

829
00:36:35.215 –> 00:36:37.045
we’re incredibly friendly and open.

830
00:36:37.725 –> 00:36:38.965
They, it’s not like, oh God,

831
00:36:38.965 –> 00:36:40.085
can I speak to that hiring manager?

832
00:36:40.085 –> 00:36:43.165
They’re gonna be so busy. They, it doesn’t matter

833
00:36:43.165 –> 00:36:45.565
who you speak to, if you’re talking to a de deputy director

834
00:36:45.625 –> 00:36:46.765
or someone at a grade three

835
00:36:46.765 –> 00:36:49.565
or four level, they, it’s very transparent

836
00:36:49.665 –> 00:36:51.005
and open and easy.

837
00:36:51.265 –> 00:36:53.445
So do reach out, um,

838
00:36:53.445 –> 00:36:55.325
because we want the best people in our teams

839
00:36:55.545 –> 00:36:58.765
and yeah, that’s just our culture, isn’t it? I think.

840
00:36:59.355 –> 00:37:00.365
Yeah, completely. It’s one

841
00:37:00.365 –> 00:37:01.645
of the reasons why I’ve stayed as well.

842
00:37:01.885 –> 00:37:04.925
I I, I, I’m constantly surprised by how everyone is

843
00:37:04.925 –> 00:37:06.365
so bloody lovely in the ea.

844
00:37:06.365 –> 00:37:08.205
Yes. Like too nice. So

845
00:37:08.205 –> 00:37:09.205
Helpful. So

846
00:37:09.205 –> 00:37:12.685
helpful. Helpful. Yeah. Yeah. Yeah.

847
00:37:14.095 –> 00:37:15.485
Kerry, we’ll come back to you now.

848
00:37:15.715 –> 00:37:18.005
Yeah, sorry. No, no, that’s good.

849
00:37:18.055 –> 00:37:19.525
We’re clearly a nice place to work.

850
00:37:20.305 –> 00:37:23.045
Um, so just a very quick question.

851
00:37:23.105 –> 00:37:25.885
And by the way, if anybody wants to submit any final couple

852
00:37:25.905 –> 00:37:28.725
of questions, please put them in now.

853
00:37:29.185 –> 00:37:31.005
Um, but this will be a quick one, Emily.

854
00:37:31.145 –> 00:37:33.125
Um, what softwares are you using for modeling?

855
00:37:34.065 –> 00:37:35.325
Ooh, that’s a very good question.

856
00:37:35.505 –> 00:37:37.925
Um, we use a lot of different software.

857
00:37:38.185 –> 00:37:40.965
So, um, Python first, first

858
00:37:40.985 –> 00:37:45.385
and foremost, um, uh, we do a bit

859
00:37:45.385 –> 00:37:47.585
of r occasionally, uh, with some of the software.

860
00:37:47.585 –> 00:37:51.225
Most of the software we run in we run is PY, Python based.

861
00:37:51.765 –> 00:37:53.585
So we use, we use a piece so called PI ppy,

862
00:37:53.615 –> 00:37:56.105
it’s like an open source water source model software.

863
00:37:56.405 –> 00:37:58.865
Um, we also use some o other software that’s actually

864
00:37:58.925 –> 00:38:01.345
for tran based in the, in the background.

865
00:38:01.445 –> 00:38:02.505
So a bit of a mix,

866
00:38:03.045 –> 00:38:05.545
but, um, quite often anyone on our team, anyone who has kind

867
00:38:05.585 –> 00:38:06.705
of any program experience.

868
00:38:06.705 –> 00:38:09.545
So I, I came in with a lot of R and matlab, um,

869
00:38:09.545 –> 00:38:12.425
and a little bit of Python, but I feel like in my opinion,

870
00:38:12.485 –> 00:38:16.065
if you’ve already learn one language, it’s very transferable

871
00:38:16.065 –> 00:38:17.065
to, to other languages.

872
00:38:17.065 –> 00:38:18.625
You can generally pick other ones up.

873
00:38:19.605 –> 00:38:21.025
Um, yeah. Okay.

874
00:38:21.685 –> 00:38:25.865
Um, one thing which I guess we, we started to talk about,

875
00:38:26.205 –> 00:38:29.785
um, but there’s any top tips for just

876
00:38:30.335 –> 00:38:34.105
translating those skills from academia to industry

877
00:38:34.125 –> 00:38:37.205
or the, I guess the top skills that you look for,

878
00:38:38.965 –> 00:38:41.945
um, within the environment agency

879
00:38:43.555 –> 00:38:46.125
from academia, which translate into industry?

880
00:38:46.235 –> 00:38:48.405
That was a question that came up a lot was pre

881
00:38:49.075 –> 00:38:50.765
Yeah, I think I touched

882
00:38:50.765 –> 00:38:51.845
on, touched on it a little bit before.

883
00:38:52.165 –> 00:38:53.925
I would say, yeah, that’s, that independent

884
00:38:53.925 –> 00:38:55.565
self-learning Yeah.

885
00:38:55.665 –> 00:38:58.765
Is key, which all postdocs do.

886
00:38:58.835 –> 00:39:00.965
It’s so solving, solving problems independently.

887
00:39:00.965 –> 00:39:02.565
So scouring literature

888
00:39:02.565 –> 00:39:05.405
and talking to everyone under the sun to inform yourself

889
00:39:05.425 –> 00:39:07.165
and, um, and,

890
00:39:07.265 –> 00:39:11.205
and with that kind of comes a confidence, um, which is

891
00:39:11.205 –> 00:39:13.245
so transferrable into industry, I think.

892
00:39:13.275 –> 00:39:17.285
Yeah, I just mentioned programming skills really, um, sought

893
00:39:17.285 –> 00:39:19.685
after, not just programming because also mapping skills

894
00:39:19.785 –> 00:39:21.365
or dealing with big data sets.

895
00:39:22.265 –> 00:39:25.325
Um, but yeah, then project project management.

896
00:39:25.585 –> 00:39:27.285
So the ability to have a huge project,

897
00:39:27.395 –> 00:39:30.565
then break it down into manageable tasks with deliverables,

898
00:39:30.565 –> 00:39:34.325
with deadlines, um, which all postdocs do.

899
00:39:34.985 –> 00:39:38.165
Um, just, they might not realize that they’re doing it,

900
00:39:38.705 –> 00:39:41.005
but it’s really important that you communicate that, um,

901
00:39:41.025 –> 00:39:42.245
in application interview.

902
00:39:42.785 –> 00:39:46.325
Um, I think experience that like mentoring undergraduates

903
00:39:46.325 –> 00:39:48.965
and master’s students is really, really helpful.

904
00:39:49.185 –> 00:39:50.405
Really good to see.

905
00:39:51.105 –> 00:39:53.305
Um, uh,

906
00:39:53.305 –> 00:39:55.545
because I guess it just shows that you can perhaps delegate

907
00:39:55.545 –> 00:39:59.385
responsibility or you can communicate with different people

908
00:39:59.385 –> 00:40:00.545
with different experience levels.

909
00:40:01.725 –> 00:40:04.545
Um, other things, again, yeah,

910
00:40:04.545 –> 00:40:05.865
it’s a lot of it’s communication as well.

911
00:40:05.885 –> 00:40:07.425
So evidence that you’ve,

912
00:40:08.285 –> 00:40:10.585
you can obviously publish scientific papers,

913
00:40:11.045 –> 00:40:13.305
but, um, perhaps report writing.

914
00:40:13.445 –> 00:40:16.505
Can you communicate that to different types of audience?

915
00:40:17.405 –> 00:40:19.825
Um, can you present, um,

916
00:40:20.285 –> 00:40:22.145
you don’t really present upstanding up these days

917
00:40:22.145 –> 00:40:23.385
or mostly presenting of teams, but

918
00:40:23.485 –> 00:40:24.585
can you present a slide pack?

919
00:40:25.325 –> 00:40:28.185
Um, that, those, those kind of things.

920
00:40:28.385 –> 00:40:31.345
Those are all, all very transferable skills from academia

921
00:40:31.455 –> 00:40:35.735
that will definitely help you succeed in industry.

922
00:40:37.015 –> 00:40:38.575
I love that point around supervision

923
00:40:38.575 –> 00:40:41.255
and the alignment to line management experience.

924
00:40:41.315 –> 00:40:44.975
That’s really, really good. Mm-hmm. Um, okay.

925
00:40:45.275 –> 00:40:47.655
We are at 1245.

926
00:40:48.035 –> 00:40:49.735
Um, so we are due to wrap up now,

927
00:40:49.735 –> 00:40:53.255
but I just wanna thank you both for the amazing insights

928
00:40:53.355 –> 00:40:54.535
and for showing clearly

929
00:40:54.565 –> 00:40:57.615
what a friendly organization the EA is.

930
00:40:58.275 –> 00:41:02.015
Um, and yes, thanks for everyone for attending.

931
00:41:02.015 –> 00:41:05.815
We will be, um, uploading a recording, um,

932
00:41:06.035 –> 00:41:07.735
or a version of this to the portal.

933
00:41:07.995 –> 00:41:10.895
Um, but yeah, thanks so much for your time everyone.