Required Skills & Experience
- Strong professional experience developing time-series forecasting models.
- Experience with wind, solar, load/demand, generation, weather, or a closely related operational forecasting use case.
- Experience working with weather data and/or Numerical Weather Prediction (NWP) forecasts.
- Strong Python experience using Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, or similar libraries.
- Strong knowledge of feature engineering, regression, ensemble modeling, backtesting, and forecast validation.
- Experience measuring forecast performance using metrics such as MAE, RMSE, and bias.
- Experience deploying and supporting machine-learning or forecasting models in production.
- Strong SQL skills; PostgreSQL preferred.
- Experience with AWS and Git.
Nice to Have Skills & Experience
- Utility-scale wind generation forecasting experience.
- Solar generation or electric load forecasting experience.
- Experience evaluating or integrating multiple weather/NWP providers.
- Experience with SCADA, telemetry, renewable generation, or weather-to-power modeling.
- Knowledge of renewable energy operations, utilities, energy markets, or ISO/RTO environments.
- Experience with probabilistic forecasting and uncertainty modeling.
Job Description
NextEra Energy is seeking a Senior Data Scientist with hands-on time-series forecasting experience to develop and improve forecasting solutions for wind, solar, and electric load, with a primary focus on wind generation forecasting.
This role requires a strong combination of machine learning, time-series modeling, weather data, and operational forecasting. The ideal candidate has built forecasting models in a professional environment and has experience working with weather/Numerical Weather Prediction (NWP) data, renewable generation, load, or another closely related physical forecasting use case.
Key Responsibilities
- Develop, validate, deploy, and optimize wind, solar, and load forecasting models.
- Build forecasting models using weather/NWP data, historical generation or load data, and SCADA/telemetry.
- Perform time-series feature engineering, backtesting, model validation, and error analysis.
- Build models using Python and techniques such as XGBoost, LightGBM, regression, ensemble methods, and statistical time-series modeling.
- Evaluate weather and NWP providers for forecast accuracy, reliability, latency, and coverage.
- Develop strategies for weather-provider blending, weighting, bias correction, and fallback.
- Monitor forecast performance using MAE, RMSE, bias, and forecast skill.
- Investigate significant forecast misses and determine whether they are caused by weather inputs, data quality, operational conditions, or model behavior.
- Deploy and monitor forecasting models in AWS production environments.
- Partner with Data Engineering, DevOps, and Operations teams to maintain reliable forecasting workflows.