Senior Data Scientist: Optimization

About the role

As a Senior Data Scientist, you will be the lead architect of the decision-making logic within our Energy Management System (EMS). You will define the mathematical core of the system: designing how it predicts, reasons, and optimizes energy flows in real-time, a complex "time-based decision" problem in the transition to green energy.

What you'll do

• Create the high-level optimization frameworks (MILP, NLP, or Stochastic Programming) to manage residential energy flows across heat pumps, thermal storage, EVs, and batteries.
• Design and tune closed-loop control strategies to ensure system stability, robustness against model/reality mismatch, and seamless integration of high-level optimization with device constraints.
• Utilize Stochastic and Learning-Based Control (e.g. Markov Decision Processes, Reinforcement Learning, or Model Predictive Control) to handle the uncertainty of weather, prices, and human behavior.
• Develop ML models that respect real-world constraints, ensuring algorithms "understand" the thermal inertia of a building or the degradation curves of a lithium-ion battery.
• Build high-fidelity simulations to validate algorithm performance against historical data before deploying code to edge devices and cloud environments.
• Architect and implement complex models from scratch in Python, ensuring they are robust enough to run in a cloud-to-edge environment.
• Act as a senior voice in technical sessions, mentoring junior team members and defining the algorithmic requirements that guide the product roadmap.
• Work closely with Energy Engineers and Backend Developers to translate math into reliable, production-grade services.

What you'll need

• Deeply familiar with mathematical optimization, with hands-on experience with solvers for MILP, NLP, or MINLP (e.g., CasADi, Gurobi, Pyomo).
• In-depth statistical knowledge and experience in time-series forecasting, specifically handling uncertainty through stochastic modeling.
• Proficient in Python and able to design complex model architectures from the ground up with end-to-end thinking.
• Comfortable with the "messy" reality of hardware — eager to learn the specifics of heat storage, hydraulic balancing, and electrical constraints.
• Able to explain the "Why" behind a complex stochastic model to a non-technical stakeholder and lead architecture brainstorming sessions.

Nice to have

• Experience in energy usage prediction, Reinforcement Learning, IoT/Edge deployments, or energy management systems (EMS).

What they offer

• Flexible working hours, home office, and remote work
• Ongoing training opportunities through job challenges, open feedback culture, and sponsored programs
• Employee benefits such as Urban Sports Club or Become1
• Regular team events