Data Scientist

Job Title: Principal Data Scientist – Load & Renewable Generation Forecasting Location: Juno Beach, FL Employment Type: Contract Job Summary We are seeking an experienced Principal Data Scientist with deep expertise in load and renewable energy forecasting (solar and wind). The ideal candidate will lead the design, development, and deployment of advanced time-series forecasting models that support grid operations, energy trading, and system planning. This role requires strong experience in integrating weather data, deploying models in cloud environments, and continuously improving forecast accuracy. Key Responsibilities • Design and develop advanced time-series forecasting models for electrical load, solar, and wind generation. • Integrate weather data (e.g., temperature, wind speed, irradiance) into forecasting models and address data quality and variability challenges. • Lead end-to-end model lifecycle: data ingestion, feature engineering, model development, validation, deployment, and monitoring. • Deploy scalable forecasting solutions in cloud environments (AWS, Azure, or GCP) using modern MLOps practices. • Collaborate with cross-functional teams including trading, operations, and system operators to align forecasting outputs with business needs. • Monitor model performance post-deployment and implement continuous improvement strategies. • Develop performance metrics, dashboards, and reporting frameworks to track forecast accuracy and reliability. • Ensure data integrity, model robustness, and compliance with industry standards. • Mentor junior data scientists and provide technical leadership on forecasting initiatives. Required Qualifications • Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Engineering, or a related field. • 8+ years of experience in data science with a strong focus on time-series forecasting. • Proven experience in load forecasting and renewable generation forecasting (solar/wind). • Strong programming skills in Python or R, with hands-on experience in libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. • Experience integrating and processing large-scale weather datasets. • Hands-on experience with cloud platforms (AWS, Azure, or GCP) and model deployment pipelines. • Solid understanding of statistical modeling, machine learning, and forecasting techniques (ARIMA, Prophet, LSTM, etc.). Preferred Qualifications • Experience in the energy or utilities domain. • Familiarity with energy markets, grid operations, and trading strategies. • Knowledge of MLOps tools such as Docker, Kubernetes, CI/CD pipelines. • Experience with big data technologies (Spark, Hadoop). • Strong problem-solving skills and ability to communicate complex insights to non-technical stakeholders. Key Skills • Time-Series Forecasting • Renewable Energy Analytics (Solar & Wind) • Weather Data Integration • Machine Learning & Statistical Modeling • Cloud Deployment & MLOps • Data Visualization & Reporting