Data Scientist

Required Skills & Experience
• Strong professional experience developing time-series forecasting models.\n\t• Experience with wind, solar, load/demand, generation, weather, or a closely related operational forecasting use case.\n\t• Experience working with weather data and/or Numerical Weather Prediction (NWP) forecasts.\n\t• Strong Python experience using Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, or similar libraries.\n\t• Strong knowledge of feature engineering, regression, ensemble modeling, backtesting, and forecast validation.\n\t• Experience measuring forecast performance using metrics such as MAE, RMSE, and bias.\n\t• Experience deploying and supporting machine-learning or forecasting models in production.\n\t• Strong SQL skills; PostgreSQL preferred.\n\t• Experience with AWS and Git.

Nice to Have Skills & Experience
• Utility-scale wind generation forecasting experience.\n\t• Solar generation or electric load forecasting experience.\n\t• Experience evaluating or integrating multiple weather/NWP providers.\n\t• Experience with SCADA, telemetry, renewable generation, or weather-to-power modeling.\n\t• Knowledge of renewable energy operations, utilities, energy markets, or ISO/RTO environments.\n • 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.\n\nThis 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.\n\nKey Responsibilities\n\t• Develop, validate, deploy, and optimize wind, solar, and load forecasting models.\n\t• Build forecasting models using weather/NWP data, historical generation or load data, and SCADA/telemetry.\n\t• Perform time-series feature engineering, backtesting, model validation, and error analysis.\n\t• Build models using Python and techniques such as XGBoost, LightGBM, regression, ensemble methods, and statistical time-series modeling.\n\t• Evaluate weather and NWP providers for forecast accuracy, reliability, latency, and coverage.\n\t• Develop strategies for weather-provider blending, weighting, bias correction, and fallback.\n\t• Monitor forecast performance using MAE, RMSE, bias, and forecast skill.\n\t• Investigate significant forecast misses and determine whether they are caused by weather inputs, data quality, operational conditions, or model behavior.\n\t• Deploy and monitor forecasting models in AWS production environments.\n\t• Partner with Data Engineering, DevOps, and Operations teams to maintain reliable forecasting workflows.