Data Scientist
Indexed description
Principal Data Scientist - Load & Renewable Generation Forecasting
Position Specific Description
The IT Forecasting team is seeking a Principal Data Scientist to lead the development and production deployment of advanced forecasting models for Load, Solar, and Wind generation across .This role supports critical decision-making for Energy Management (EMT), Power Marketing (PMI), and System Operations (Sysops FPL).
This position will engage in end-to-end model development—from conception through production deployment, including advanced feature engineering, weather data integration via APIs, model optimization, and performance monitoring. The ideal candidate will design production-ready forecasting solutions integrated with internal systems, AWS cloud infrastructure, implementing robust error handling, alerting mechanisms, and recovery procedures.
The successful candidate should possess deep expertise in time-series forecasting, machine learning operations, and building scalable data pipelines. They must demonstrate the ability to transform complex datasets into actionable forecasts that drive real-time operational decisions in the energy sector.
We are looking for someone who has a passion for solving challenging forecasting problems and can deliver production-grade solutions in a dynamic operational environment with stringent accuracy requirements and time-sensitive obligations.
Key Expectations
- Understands business priorities and takes ownership of the complete model lifecycle from development to production deployment
- Demonstrates strong communication and collaboration skills working with trading, operations, and engineering teams
- Works effectively within Agile frameworks and actively participates in scrum ceremonies
- Deliver cost-effective, high-quality forecasting solutions that meet operational deadlines and accuracy standards
- Python Expertise: Advanced proficiency in Python with extensive experience in data science libraries (pandas, numpy, scikit-learn, statsmodels)
- Time-Series Forecasting: Proven track record developing and deploying production forecasting models (ARIMA, SARIMAX, gradient boosting methods)
- Production Deployment: Demonstrated experience taking models from research/development through production deployment with proper versioning, monitoring, and maintenance
- API Integration: Experience consuming and integrating weather APIs and external data sources into forecasting pipelines, with a strong background in data engineering
- Cloud & Infrastructure: Working knowledge of AWS services (EC2, S3, Lambda, SageMaker) and Infrastructure-as-Code practices
- Database Management: Proficiency with SQL databases and experience with large-scale data queries and optimization
- Dashboard Development: Experience building interactive dashboards using Streamlit, Plotly, or similar frameworks for stakeholder communication
- Advanced feature engineering, uncertainty quantification, and probabilistic forecasting methods
- Energy markets and renewable generation forecasting domain expertise
- Agile project management (Jira, Confluence)
- Feature engineering with weather variables and domain-specific signals
- Model evaluation metrics (MAE, RMSE, MAPE)
- Automated data pipelines and version control (Git)
- Lead end-to-end forecasting model development for Load, Solar, and Wind from conception through production deployment
- Build automated retraining and evaluation frameworks with monitoring dashboards
- Develop data connectors for weather APIs and data warehouse systems
- Design scalable pipelines for real-time and batch forecasting operations
- Create interactive dashboards (Streamlit) and present insights to stakeholders
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