Own the technical direction for pricing ML, defining what to build and how within the pricing engine and setting the strategy and roadmap for this core piece of our IP.
Build ML systems for price optimisation, designing and implementing models that dynamically set prices while balancing signing probability, portfolio balance, and margin maximisation.
Solve imbalance problems by developing probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment.
Bridge modelling and production by owning the modelling and data layer while working closely with software engineers and MLOps to ensure models are architected for production and contribute to system design decisions that affect performance and reliability.
Communicate pricing decisions clearly by articulating model behaviour, assumptions, and trade-offs to technical stakeholders so pricing decisions are understood across dependent teams.
AI
Support
Python
Machine Learning
PyTorch

Deep experience building ML systems for pricing, revenue optimisation, or decision-making under uncertainty, with a track record of models that went from concept to production and delivered measurable commercial impact.
Strong foundation in stochastic optimisation and probabilistic modelling, with the judgement to formulate ambiguous business problems as the right mathematical approach rather than reaching for familiar tools.
Proven first-principles reasoning: we choose between stochastic programming, classical ML, reinforcement learning, or a simple heuristic based on the problem, not the technique we know best.
The engineering craft to match our modelling depth: production-grade Python, a high bar for code quality and system design, and the ability to work alongside software engineers as a technical peer across the full ML lifecycle.
Senior technical leadership in ML: a track record of setting direction for a significant technical area, influencing cross-functional teams, and translating complex model decisions into clear terms for commercial, product, and engineering stakeholders so they are understood and acted on.
Experience with reinforcement learning or causal inference in applied, commercial settings.
Familiarity with energy markets, power trading, or portfolio management.
PhD or equivalent research depth in a quantitative discipline such as statistics, applied mathematics, physics, operations research, or similar.
Ability to reason about the trade-offs between optimisation solvers and gradient-based ML methods, and the judgement to know when to use each.
Experience working with high data throughput systems in production.

1 Tem, Inc jobs in England & Wales