Data Scientist
Core
Building forecasting and decision-making models for household electricity demand, solar generation, and battery behavior in data-sparse new-build home environments.
Role type
Lead Data Scientist (Time-Series Forecasting & Optimization)
Builds
Short-horizon forecasts for real-time energy optimization algorithms
Domain
Renewable energy, smart grids, residential solar and battery storage
Deliverable
production ML models
Required skills
Time-series forecasting, probabilistic modelling, feature engineering, synthetic data generation, transfer learning, Python (scikit-learn, statsmodels, Prophet, PyTorch), data pipeline maintenance, model validation and benchmarking
Preferred skills
Smart meter data analysis, distributed energy resources knowledge, grant-funded research experience, cloud infrastructure (AWS, GCP, Azure)
Responsibilities
Develop cold-start models using proxy/synthetic data, create intra-day to multi-day forecasts, validate model performance against baselines, iterate models with live operational data, collaborate with Technical Lead on optimization engine integration, contribute to performance analysis (cost savings, carbon impact)
Seniority
Individual Contributor (Lead/First Hire)
