Senior Data Scientist – Load & Renewable Generation Forecasting

Senior Data Scientist – Load & Renewable Generation Forecasting

Position Specific Description

The IT Forecasting team is seeking a Senior Data Scientist to develop, enhance, and productionize forecasting solutions supporting Wind, Solar, and Load forecasting across NextEra Energy operations .

The primary focus of this role will be Wind generation forecasting , while also providing technical support across Solar and Load forecasting initiatives.

This is a hands-on role combining forecasting, meteorology, renewable-energy analytics, machine learning, data engineering, and production support . The successful candidate will independently develop and improve forecasting models from data exploration and feature engineering through validation, deployment, monitoring, and ongoing optimization.

A key responsibility will be evaluating and optimizing the use of weather and Numerical Weather Prediction (NWP) providers . The candidate will compare provider accuracy, available variables, forecast horizons, geographic coverage, latency, reliability, and overlap between services to determine the most effective weather inputs for each forecasting application.

This includes identifying where individual providers or weather features perform better under specific conditions and identifying opportunities to reduce unnecessary provider redundancy while maintaining forecast accuracy and resiliency.

The ideal candidate combines strong statistical and machine-learning skills with practical understanding of the meteorological, physical, and operational factors affecting renewable generation and electric demand.

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

Load

• Temperature, humidity, heat index, and other weather drivers
• Calendar and seasonal patterns
• Lagged demand and weather relationships
• Extreme-weather behavior
• Regional and customer-segment differences
• Peak-demand forecasting

The candidate should understand that observed generation and demand can be influenced by

operational conditions in addition to weather

and account for these factors when developing, training, and evaluating forecasting models.