chdove
Yes, the 25/26 Premier League Champions! You'd be embedded with the Men's First Team analysis dept building the internal tooling analysts and coaches use day to day — full-stack apps plus applied ML to automate and augment football analysis. The fun part is the constraint: it has to be genuinely useful to coaching staff and hold up against a live training/matchday schedule, not just be technically neat.
Python / SQL / TypeScript, PyData stack (Pandas/Polars, Altair, etc.), PyTorch/JAX/TF. You'll own things end-to-end, data through UI.
Looking for strong quant background, first-principles engineering judgment, and taste in how insights get presented. Here's a taste of what the work looks like: https://x.com/karun1710/status/2080382035613405584?s=20
Hybrid, DBS check and UK Right to Work required. Closes 6 Aug 2026. Apply: https://careers.arsenal.com/jobs/8083296-research-engineer
https://www.theguardian.com/football/2014/oct/17/arsenal-pla...
> Does your team and department taking of analytics primarily on the footballing side? Like player performance?
Yes, we work primarily on the footballing side and across the spectrum in that space: Player/team performance for the men's first team, women's first team, and boys academy age groups U16 and up, player recruitment / squad planning, etc.
> How does your teams work typically get incorporated
We produce a mix of interactive tools, regular static reports (e.g. opposition analysis, post-match analysis, etc.), and live dashboards that come from specific stakeholder requests such as coaching staff or execs, or that we build proactively to address a specific football-related question.
> what does the day to day look like?
It really varies from day to day and role to role within the team. A data engineer might be adding another data provider to an entity resolution ETL pipeline, a research scientist might be incorporating feedback from first team coaching staff into a work-in-progress model, a data analyst might be putting together an in-depth opposition analysis report for an upcoming match, and an operations analyst might be helping train operators on a new data labeling task.
> Do you manage your own tech stack as well?
We do manage most of our tech stack, although we get a lot of support on front-end from a great sister team in the IT dept.
Regardless of their sophistication, 3rd party data products in football tend to rely on manually collected and maintained player metadata. It can be unreliable. If I could reliably have a durable unique ID for every player, manager, and team in world football along with reliable timestamps for every moment each said player entered and left play, that would be pretty great. When joining together disparate data sources, discrepancies in things this simple cause all sorts of pain downstream.
It has already had a big impact for ball crossing the goal line judgments if you put computer vision in the broader AI category (e.g. Hawkeye in the PL)
I’m Chris, Head of Analytics at Arsenal and hiring manager for this role (https://www.linkedin.com/in/chris-dove-43b7a85)
I’m happy to answer any questions about the role, our department, football analytics, working at Arsenal, etc.
Aside from 3rd party data, we contract with a nonprofit in Laos where operators collect event data for us (~2000 data points per match) across roughly a dozen leagues worldwide.
We also extract player tracking data from video.
While living off of porridge shouldn’t be necessary, I will say the porridge at the training ground is absolutely top tier.