Data Scientist

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Who We Are:

We’re rebuilding the energy transaction system, making it transparent and fair. tem exists to put power back in the hands of people. Today’s wholesale energy market is stacked in favour of the few. It's a product of an age of oil and gas, riddled with markups and middlemen. We’re changing that. Our product, RED™, built on a proprietary pricing engine that bypasses the wholesale market, enables businesses to buy the energy produced by renewable generators directly.

That's 100% transparent transactions, ensuring affordable bills and fair compensation, to give every business ownership and control over where their energy comes from. Since launching in 2021, we’ve saved UK businesses and generators over £20 million, powering a growing network of forward‑thinking companies, from Pizza Pilgrims to Silverstone. Backed by top‑tier VCs such as Atomico and Albion, we’re creating a new category in energy - one that’s local, decentralised, and built on trust.

Compensation range: £105,000.

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The Role:

Do you want to work on one of the hardest and most important problems in energy: how to allocate, price, and fulfil renewable electricity contracts efficiently at scale? Energy markets today are opaque, inefficient, and expensive. At tem, we’re building the intelligence layer that reduces the cost of transacting electricity and unlocks access to renewables - by owning the core algorithms that sit at the heart of pricing, matching, and risk.

We’re looking for a Staff Data Scientist to play a key hands‑on role in developing Rosso, our proprietary pricing and allocation engine, and adjacent optimisation systems such as P442 matching and Red Score logic. This role is focused on first‑principles modelling, optimisation, and production‑grade ML systems, with real commercial impact. You’ll work on greenfield problems where there is limited precedent, helping bring core optimisation IP fully in‑house and into production - reducing dependency on third parties and shaping how energy markets operate at scale.

• Own and build core optimisation systems: design, implement, and operate ML and optimisation models that power pricing, matching, and allocation within Rosso and related systems, from research through to production.
• Solve complex applied problems: develop linear programming and optimisation solutions for batch and near‑real‑time use cases, balancing cost, risk, portfolio constraints, and commercial outcomes.
• Ship production‑grade models: build and maintain end‑to‑end ML and optimisation pipelines in the cloud (AWS preferred), ensuring robustness, explainability, and operational reliability.
• Develop core IP in‑house: replace third‑party logic with high‑quality internal implementations, iterating quickly as product and market understanding evolves.
• Collaborate and influence: work closely with product, engineering, and commercial teams to translate business needs into effective technical solutions, communicating clearly with non‑technical stakeholders.
• Raise engineering standards: contribute to best practices in modelling, experimentation, and code quality, and provide informal mentorship to junior engineers and data scientists.

Must‑haves

• Strong optimisation background: experience with linear programming, operations research, or constrained optimisation in real‑world systems.
• Hands‑on ML / data science experience: proven ability to build and ship models that matter.
• Production mindset: experience designing, deploying, and maintaining cloud‑based ML or optimisation systems.
• First‑principles thinking: comfortable working in ambiguous, greenfield problem spaces.
• Strong Python skills and experience with the standard data science stack.
• Commercial awareness: understands how technical decisions translate into business impact.

Nice‑to‑haves

• PhD or equivalent experience in applied maths, operations research, or machine learning.
• Experience with pricing systems, allocation problems, or risk modelling.
• Familiarity with energy markets, trading, or infrastructure‑heavy domains.
• Experience with time series forecasting, Bayesian methods, or…