Principal Data Scientist

Summary: 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.

Kforce has a client that is seeking a Principal Data Scientist in Juno Beach, FL. Summary: 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. 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

Job Duties & Responsibilities: Model Development & Production:

• 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

Data & Infrastructure:

• 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

Requirements:

• API Integration: Experience consuming and integrating weather APIs and external data sources into forecasting pipelines, with a strong background in data engineering
• Dashboard Development: Experience building interactive dashboards using Streamlit, Plotly, or similar frameworks for stakeholder communication
• Production Deployment: Demonstrated experience taking models from research/development through production deployment with proper versioning, monitoring, and maintenance
• Cloud & Infrastructure: Working knowledge of AWS services (EC2, S3, Lambda, SageMaker) and Infrastructure-as-Code practices
• 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)
• Database Management: Proficiency with SQL databases and experience with large-scale data queries and optimization

Preferred Skills & Experience:

• Advanced feature engineering, uncertainty quantification, and probabilistic forecasting methods
• Energy markets and renewable generation forecasting domain expertise
• Agile project management (Jira, Confluence)

Technical Competencies:

• Feature engineering with weather variables and domain-specific signals
• Model evaluation metrics (MAE, RMSE, MAPE)
• Automated data pipelines and version control (Git)

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.