Data Scientist – Load & Renewable Generation Forecasting

eTeam is recruiting for a Senior Data Scientist role supporting forecasting across NextEra Energy operations. The role focuses on developing, optimizing, deploying, and monitoring wind generation forecasts, while also supporting solar and load forecasting and evaluating weather and Numerical Weather Prediction provider performance.

Responsibilities

• Develop and enhance Wind, Solar, and Load forecasting models from research and backtesting through production implementation
• Build forecasting approaches using combinations of:
• Perform feature engineering, model selection, optimization, and validation
• Develop backtesting and benchmarking processes to evaluate forecast performance across sites, horizons, seasons, and operating conditions
• Investigate forecast errors and determine whether they originate from weather, model behavior, data quality, or operational conditions
• Improve existing and legacy forecasting models while identifying opportunities to simplify and modernize model architecture
• Analyze multiple weather and NWP providers for accuracy, reliability, coverage, latency, available variables, forecast horizon, and operational value
• Compare provider performance by:
• Identify the relative strengths and weaknesses of individual weather providers and determine where specific providers or variables may provide superior forecasting value
• Assess redundancy and overlap across weather services and support recommendations for an efficient core set of providers
• Develop and evaluate approaches for:
• Ensure correct alignment of weather forecast issue times, valid times, time zones, spatial resolution, measurement units, and forecast horizons
• Monitor weather-data quality and identify provider anomalies or degradation that could impact production forecasts
• Apply relevant physical, meteorological, and operational knowledge across forecasting applications
• Monitor forecast performance using metrics such as MAE, RMSE, normalized error, bias, forecast skill, and probabilistic metrics where appropriate
• Benchmark internal forecasts against persistence, existing models, alternative weather providers, and third-party forecasts
• Develop monitoring and diagnostics for:
• Implement appropriate logging, alerts, error handling, and recovery procedures
• Support forecasting solutions designed to continue operating when individual data feeds or weather services become unavailable
• Perform root-cause analysis of significant forecast misses and recommend corrective actions
• Develop data connectors and pipelines supporting weather, telemetry, historical generation, load, and operational data
• Support scalable batch and near-real-time forecasting workflows
• Maintain clear model configuration, versioning, dependencies, and documentation
• Collaborate with data engineering and DevOps teams to deploy and maintain forecasting applications
• Build dashboards and analytical tools using Streamlit, Plotly, or similar technologies to communicate forecast performance, model behavior, and operational impacts

Skills

• Experience : 8 to 10 Year
• Advanced proficiency in Python and common data-science libraries such as pandas, NumPy, scikit-learn, statsmodels, LightGBM, XGBoost, or equivalent tools
• Demonstrated experience developing and deploying forecasting, time-series, or predictive models
• Strong knowledge of:
• Feature engineering
• Regression and time-series methods
• Gradient boosting
• Ensemble forecasting
• Probabilistic or quantile forecasting
• Model validation and backtesting
• Experience integrating and analyzing weather APIs, NWP forecasts, SCADA/telemetry, databases, and external data feeds
• Ability to evaluate weather-provider performance and determine its impact on forecast accuracy
• Strong SQL and data-analysis skills
• Experience transitioning models from research and development into reliable production workflows
• Working knowledge of AWS services such as EC2, S3, Lambda, ECS, Step Functions, CloudWatch, and SageMaker, or comparable cloud technologies
• Experience with Git, version control, testing, and reproducible software-development practices
• Strong troubleshooting, analytical, communication, and documentation skills
• Ability to work independently while collaborating effectively with data scientists, data engineers, DevOps, operations, trading, and business stakeholders

Company Overview

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