Data Scientist
Core
Develop market forecasts and predictive models to optimize battery storage trading operations across wholesale and ancillary electricity markets in GB.
Role type
Applied Data Scientist (Energy Trading & Optimization)
Builds
Parallelised predictive models for price/volume forecasting, automated trading applications, and risk visualization dashboards.
Domain
Energy Trading, Renewable Energy Management, Battery Storage (BESS)
Deliverable
production ML models
Required skills
Time-series modelling, Tree-based & gradient boosting models, Python (production level), Dashboard building, Data cleaning & feature engineering, Requirements elicitation, Technical documentation
Preferred skills
Simulation-Optimization Integration, Stochastic Programming, Battery Storage technology awareness, Renewable Energy Generation operations, Energy Storage trading experience
Technologies
Python, polars, pydantic, uv, SQLAlchemy, Streamlit, Postgres, prefect, Kubernetes, AWS, lightgbm, xgboost, numpy, scipy, scikit-learn, Grafana, Superset, Marimo
Responsibilities
Develop market forecasts for trading teams; Build, prototype, test, and scale parallelised predictive models; Clean complex datasets and engineer temporal features; Create actionable insights to improve trading performance and manage risk; Visualize and communicate insights for high reward vs risk decisions; Work with tech teams to source data and support productionisation.
Seniority
Mid-Senior, hands-on IC