My client is a venture-backed data and analytics business. They take large, messy real-world datasets, run their own prediction models over them, and sell the output as a SaaS product to professional users around the world who rely on it for time-sensitive decisions.
They’re hiring a Principal Data Scientist to be the senior data science authority in the business. It’s a hands-on individual contributor role with no direct reports, reporting into a VP-level data leader. A lot of the work is on problems nobody has solved yet, where there’s no existing baseline and you’d be the one deciding what good looks like.
What you’d be doing
- Setting data science direction at a product level, deciding what gets built and why
- Owning models end to end, from research and experiment design through to production, monitoring and long-term maintenance
- Working mostly on time-series and anomaly detection, alongside sequence-to-sequence models and stochastic optimisation
- Improving the quality of models that are already live
- Leading the company’s Data Science Guild, mentoring and reviewing work across the practice
- Explaining and defending your models to traders, analysts and non-technical colleagues
What you’ll need
- Experience operating at Staff or Principal level, with the autonomy that comes with it
- Deep grounding in ML theory and the maths behind it
- Fluent, production-standard Python
- A track record of putting models into production and owning them afterwards
- Comfort working on open-ended problems with no agreed definition of success
Nice to have
- Knowledge of energy markets or energy systems
- Quant trading experience, such as arbitrage, backtesting or risk
- Hands-on work with transformer architectures, generative models or agentic AI
Compensation
The base range is £120k to £150k. Everyone in the business holds equity, and you’ll be shown a model of the projected upside during the process. The role is hybrid, with 2 days a week in the London office.
Interview Process
The interview process has three stages: a conversation with the VP of Data Production, a technical take-home, and a final discussion with the CEO.
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