Overview

Our client is a leading force in the North American energy sector, committed to innovation and sustainability. We are seeking a Principal Data Scientist to join a critical team focused on forecasting for load and renewable generation. This is a high-impact, contract role where you will develop and deploy advanced forecasting models that directly influence real-time operational decisions, market strategies, and grid reliability for one of the nation's largest energy portfolios.

You will be at the intersection of data science, energy operations, and cloud technology, building production-grade solutions that handle the complexities of weather-dependent renewable energy and dynamic electricity demand.

Key Responsibilities

• Lead the end-to-end development, optimization, and production deployment of advanced time-series forecasting models for electricity load, solar generation, and wind generation.
• Integrate complex datasets, including high-frequency weather data from APIs, into robust forecasting pipelines.
• Design and implement scalable data pipelines and machine learning operations (MLOps) frameworks within AWS cloud infrastructure.
• Collaborate with Energy Management, Power Marketing, and System Operations teams to translate business requirements into technical specifications and actionable forecasts.
• Establish robust model monitoring, error handling, alerting mechanisms, and recovery procedures for 24/7 operational systems.
• Conduct advanced feature engineering and model validation to ensure forecasts meet stringent accuracy and reliability standards.
• Document methodologies, present findings to technical and non-technical stakeholders, and provide mentorship on best practices.

Requirements

• Experience: 6-10 years of professional experience in data science, with a proven track record in time-series forecasting.
• Technical Expertise: Deep expertise in Python (Pandas, NumPy, Scikit-learn, Statsmodels, PyTorch/TensorFlow) and SQL.
• MLOps & Cloud: Hands-on experience building production ML systems, including deployment, monitoring, and scaling on AWS (S3, Lambda, SageMaker, EC2, etc.).
• Domain Knowledge: Strong understanding of forecasting methodologies (ARIMA, Prophet, LSTM, Gradient Boosting) and experience with weather data integration.
• Systems Integration: Experience designing and implementing APIs and data pipelines that integrate with enterprise systems.
• Mindset: Passion for solving complex, high-stakes problems in a dynamic, time-sensitive environment. Strong ownership of projects from conception to production support.
• Education: Master's or PhD in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field is highly preferred.