Career journey interview: David Ashmore

Dr. David Ashmore was a member of Prosper's first pilot cohort. Following the end of the cohort, he went onto become a Remote Sensing Scientist at the Met Office. He then transitioned to a new role within the organisation: Scientific Software Engineer (Machine Learning Datasets). In March 2025 we checked back in with David to learn more about his career journey. The full interview is below:

What is your current role?

I work at the Met Office, the UK’s national weather forecasting centre, where my job title is “Scientific Software Engineer – ML Datasets” (with ML standing for machine learning).

What does it involve?

In the last couple of years machine learning approaches to weather forecasting have shown incredible, potentially revolutionary capabilities – and these approaches need very-large high-quality datasets of past weather to “learn” from.

However, the historical data volumes held at forecasting centres are not immediately suitable for this purpose: they may have, for example, inconsistencies or gaps relating to past upgrades, or be held in an older and/or slower data format.

The team I work in is responsible for curating and generating datasets suitable for input into machine learning algorithms.

My day-to-day involves writing code to assess data quality, improve, archive, move, or harmonise datasets as required; collaborating internally and externally to understand what our machine learning engineers need, and the status of specialist datasets used in other arms of the organisation - all while following software engineering and quality assurance best practice.

How has your career journey beyond academia progressed so far?

I started in the Met Office in December 2022 as a scientist focusing on scientific observations from UKRI’s atmospheric research aircraft. I worked on several measurement campaigns of cloud processes and properties and developed an improved method of processing the remote sensing datasets we collected.

The Met Office decided to wind down this arm of its business in 2024. At the same time, it increased its capacity in machine learning. While I hadn’t planned to switch jobs, this new focus presented an opportunity.

I find the nascent area of machine learning particularly interesting, and it represented a chance for me further develop skills and themes from my previous roles both inside and beyond academia.

Is there anything you picked up during the Prosper cohort that you're still applying?

Yes - particularly around themes of taking ownership of training and career direction. I continue to treat technical, communication, and interpersonal skills as equally important in my science and software-focused role: something I often forgot before engaging with Prosper.

How have you found the transition from academia to a non-academic setting?

The Met Office, as a UK government trading fund, is an interesting mix between academia and a revenue-generating business that suits me well.

I continue to collaborate with university-based staff, but there is a clear focus on contributing to the core business products and services. From a technical point-of-view I have had the opportunity to learn from lots of very talented people, and I find satisfaction in the rigour and quality of the work we’re doing.

The organisation also invests a lot in people development - there are plenty of opportunities for upskilling and career development, which I’m keen to take full advantage of.

What are your future career plans?

I plan to see where machine learning takes weather forecasting and contribute wherever I can!

What advice would you give to a postdoc in your former position, who might be interested in making a similar switch/entering your sector?

In general, I think it’s good to not assume you know how public sector or commercial organisations that deal with environmental monitoring or forecasting function.

There are a lot of different aspects to these centres - encompassing commercial, public-facing, operational, scientific, and technical aspects - and it can be daunting to begin to understand how they work together.

The plus side is that it means there is a lot of scope for finding something with a day-to-day that fits your interests and preferences.

You can read David's previous case study from his time with Prosper here.

You can find more information about careers at the Met Office here.