Quantitative Energy Analyst / Forecasting & Stochastic Optimization(m/f/d)
Tmh · München
Apply directly on Tmh’s careers site — no account needed.
About the role
Why The Mobility House?
- Help shape our vision of an emission-free energy and mobility future
- High levelof responsibility and rapid development in a growing and innovative company
- Open, diverse & international team
- Flexible working hours and additiona lvacation days
- Mobile working from home and 20 days in other European countries
- Choice between the latest Apple and Dell IT equipment
- Subsidy for Deutschlandticket job
- Wellpass membership
- Lease of your preferred car and bike via FINN or JobRad
- Modern office with good public transport connections
- and much more!
What you do
Forecasting & Stochastic OptimizationSolar forecasting
- Design, build, and deploy our end-to-end solar generation forecasting pipeline, integrating Numerical Weather Prediction (NWP) outputs (e.g., ECMWF, DWD), site-specific metadata, panel degradation curves, and live telemetry feeds.
- Partner with the Trading Platform team to define concrete data engineering requirements, ensuring high-throughput, low-latency pipelines and infrastructure.
- Establish robust forecast validation frameworks and KPIs to drive continuous on model improvements.
- Develop predictive models that capture the complex, weather-driven relationships between renewable generation peaks and intraday spot price dynamics.
- Translate forecasts into systematic trading signals that maximize intraday continuous (IDC) trading performance and mitigate market exposure risks.
- Contribute to our stochastic dynamic programming framework for optimal BESS and co-located asset dispatch.
- Formulate, backtest, and deploy high-fidelity quantitative signals directly into our algorithmic trading engine.
- Work closely our quantitative Trading Desk, actively reviewing, auditing, and improving our collective modeling approaches.
- Seamlessly translate complex mathematical outputs into high-conviction, actionable insights for the Trading Desk, and coordinate system integrations with the Platform team.
- Play a key role in scaling our predictive modeling frameworks to new European markets as co-location expands.
Who you are
- M.Sc. or Ph.D. in a highly quantitative discipline (Statistics, Mathematics, Physics, Data Science, Econometrics, or similar).
- 3+ years of experience in quantitative modeling in energy or an adjacent field, on advanced forecasting, statistical regression, or machine learning frameworks (using tools like scikit-learn, statsmodels, Nixtla, Darts or PyTorch).
- Proficiency in processing multi-dimensional and large-scale datasets (using data manipulation tools like pandas/polars, numpy, JAX or xarray) combined with sound data validation and time-series backtesting practices.
- Proven writing clean, modular, and testable code, taking models successfully from development notebooks into live production environments.
- Understanding of physical and financial European power market mechanics (Day-Ahead, Intraday/XBID, and Ancillary Services/Balancing).
- Proven ability to work autonomously, think critically, and drive complex projects end-to-end.
- Fluent in English (both written and spoken).
- Experience working with raw meteorological and NWP data (ECMWF, DWD, ERA5).
- Understanding of solar irradiance modelling and PV yield estimation.
- Exposure to mathematical programming (MILP, Stochastic Dynamic Programming, or Model Predictive Control) using algebraic modeling tools (e.g., Pyomo, CVXPY) and professional solvers (e.g., Gurobi).
- Experience building short-term trading signals or price forecasts (e.g., intraday price spreads, imbalance pricing) in volatile power markets.
Are you a good fit for us?
Diversity enriches our team and makes us stronger. Regardless of origin, gender, age, sexual orientation, religion, or any disability – everyone is welcome here! We look forward to receiving your application and achieving great things together.Your contact person
Vanessa
career@mobilityhouse.comSkills
- Python
- pandas
- NumPy
- scikit-learn
- PyTorch
- SQL
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