Staff Data Scientist, tem

• 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
• If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for

Desirable

• 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

• Our processes normally take around 3-4 weeks from first call to offer - please let us know about any adjustments to timelines that may be required
• First call with our Talent Team (30 min). This is to understand your experience, motivations, and discuss the role in more detail
• Behaviour Interview with our Head of Data (45 min). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working
• Technical Interviews with the Team (90 mins). You’ll meet members of the team, and one of our Co-Founders, to dig into your technical skills around modelling and machine learning engineering
• Culture-Add Interview with Stakeholders (45 min). The final session will be with our CEO and CTO, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it’s really like to work at tem