Lead Data Scientist

Key Responsibilities: Forecasting Model Development: Develop and enhance Wind, Solar, and Load forecasting models from research and backtesting through production implementation. Build forecasting approaches using combinations of: Weather and NWP forecasts Historical generation and load SCADA and operational telemetry Persistence and statistical baselines Physical relationships Gradient boosting and other machine-learning techniques Ensemble and probabilistic forecasting methods 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. Weather Provider Analytics Optimization: Analyze multiple weather and NWP providers for accuracy, reliability, coverage, latency, available variables, forecast horizon, and operational value. Compare provider performance by: Location/site Forecast horizon Weather variable Season Weather regime or event type 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: Weather-provider blending and ensembles Provider weighting and selection Bias correction Feature selection Provider fallback and substitution 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. Energy Forecasting Domain Knowledge: Apply relevant physical, meteorological, and operational knowledge across forecasting applications.

Required Skills: Advanced proficiency in Python and common data-science libraries such as pandas, Num Py, 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. 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, Cloud Watch, and Sage Maker, 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, Dev Ops, operations, trading, and business stakeholders. Model Performance Production Support: 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: Forecast degradation Data-quality issues Weather-provider outages or anomalies Model drift Operational impacts 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. Production Data Integration: 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 Dev Ops 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.

Preferred Experience: Experience with utility-scale wind forecasting or renewable generation modeling. Experience with multiple commercial or public weather/NWP providers. Experience working directly with wind-turbine, solar-plant, or SCADA telemetry. Experience with turbine power curves and weather-to-power conversion methodologies. Experience benchmarking internal forecasts against commercial forecasting vendors. Familiarity with energy markets, utility operations, generation scheduling, trading, or ISO/RTO environments. Experience developing forecasts across intra-hour, day-ahead, and multi-day horizons. Experience with probabilistic forecasting and forecast uncertainty. Experience supporting or mentoring less-experienced data scientists.