Description

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.

Wind

• Wind speed and direction
• Hub-height adjustments
• Vertical wind shear
• Temperature, pressure, humidity, and air density
• Turbine power curves
• Cut-in, rated, and cut-out behavior
• Wake and site effects
• SCADA and generation telemetry
• Curtailment, derates, outages, maintenance, and equipment availability

Solar

• Irradiance and cloud conditions
• Ambient and module temperature
• Inverter performance and availability
• Tracker position and stowing
• PPC/plant-controller impacts
• Curtailment
• Equipment outages
• Soiling and degradation
• Maintenance
• Site capability versus nameplate capacity
• SCADA and plant telemetry

Required Skills & Experience

• 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.

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 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.

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

Key Skills

Education

Bachelor's degree