tem
51 - 200 employees
Founded 2021
⚡ Energy
🛍️ eCommerce
☁️ SaaS
Energy • eCommerce • SaaS
tem is a company that empowers businesses to save on energy costs by directly purchasing from renewable energy generators. With its innovative RED™ product, tem simplifies the energy procurement process, offering transparency and a streamlined portal for managing contracts, quotes, and billing. The company aims to reduce the expense associated with traditional energy suppliers, enabling clients to cut energy costs by up to 25% while supporting the transition to renewable energy. tem's mission is to create accessible renewable energy solutions for all businesses, promoting sustainability and fair pricing within the energy market.
Principal MLE – Revenue Optimisation
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tem
51 - 200 employees
Founded 2021
⚡ Energy
🛍️ eCommerce
☁️ SaaS
Energy • eCommerce • SaaS
tem is a company that empowers businesses to save on energy costs by directly purchasing from renewable energy generators. With its innovative RED™ product, tem simplifies the energy procurement process, offering transparency and a streamlined portal for managing contracts, quotes, and billing. The company aims to reduce the expense associated with traditional energy suppliers, enabling clients to cut energy costs by up to 25% while supporting the transition to renewable energy. tem's mission is to create accessible renewable energy solutions for all businesses, promoting sustainability and fair pricing within the energy market.
📋 Description
• Own the technical direction for pricing ML. Define what to build and how. Set the roadmap for the pricing engine as a core piece of tem's IP — and be accountable for its performance. • Formulate and solve the pricing problem properly. The mathematical foundation doesn't fully exist yet. Your first job is to define it: a dynamic, real-time system that simultaneously optimises for signing probability, portfolio balance, and margin. Choose the right approach — stochastic programming, reinforcement learning, classical ML, or a hybrid — based on the problem, not familiarity. • Build and ship models end-to-end. Own the modelling and data layer. Write production-grade Python. Architect models with deployment in mind and carry them through to production — you can execute without being blocked by engineering dependencies. • Solve imbalance problems. Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. • Be the voice of pricing ML across the business. Commercial, product, and engineering teams depend on this engine. They need to understand what it's doing and why. You make that happen — clearly, without losing precision.
🎯 Requirements
• Deep experience building ML systems for pricing, revenue optimisation, or real-time decision-making — at companies where pricing is the product, not a supporting function. Track record of models that reached production and moved commercial metrics. • Strong foundation in stochastic optimisation and probabilistic modelling. The judgement to formulate ambiguous business problems mathematically before reaching for a tool. • First-principles reasoning across methods. You choose between stochastic programming, reinforcement learning, classical ML, or a simple heuristic based on what the problem demands. • The engineering depth to match your modelling. Production-grade Python, high bar for code quality, and the ability to carry models from formulation to deployment without being blocked. • Senior technical leadership. A track record of setting direction for a significant technical area, influencing cross-functional teams, and translating complex model behaviour into clear terms for commercial, product, and engineering stakeholders — so decisions are understood and acted on. • Experience with real-time pricing at scale — ride-hailing, food delivery, logistics, or similar environments where latency and portfolio effects matter. • Familiarity with energy markets, power trading, or portfolio risk management. • PhD or equivalent research depth in a quantitative discipline — statistics, applied mathematics, operations research, or similar. • Ability to reason about trade-offs between optimisation solvers (Gurobi etc.) and gradient-based methods (PyTorch etc.), and the judgement to know when to reach for each. • Experience with causal inference or reinforcement learning in applied commercial settings.
🏖️ Benefits
• Offers Equity
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