Quantitative Energy Analyst / Forecasting & Stochastic Optimization

The Mobility House GmbH
München, Germany
6 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Shift work
Languages
English
Experience level
Intermediate

Job location

Remote
München, Germany

Tech stack

Algorithmic Trading
Big Data
Data Validation
Information Engineering
Data Transformation
Mathematical Programming
Regression Analysis
NumPy
TensorFlow
PyTorch
Pandas
Scikit Learn
Statistics Packages
Dynamic Programming

Job description

  • 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 Optimization Solar 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.

Price & market signal forecasting

  • 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.

Stochastic optimization & algo signals

  • 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.

Cross-functional collaboration

  • 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.

Requirements

  • 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).

Nice to have:

  • 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.

Benefits & conditions

Pulled from the full job description

  • Flexible schedule

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