Lead Data Scientist

Job Summary We are seeking a Lead Data Scientist to design, develop, and deploy advanced forecasting models for load, solar, and wind generation. This role involves end-to-end ownership of the model lifecycle, from data ingestion and feature engineering to production deployment and monitoring, supporting real-time operational and strategic decision-making. Key Responsibilities Lead the development and deployment of forecasting models for load, solar, and wind generation. Design and implement end-to-end machine learning pipelines, including feature engineering, model training, and evaluation. Integrate external data sources such as weather APIs into forecasting workflows. Develop scalable data pipelines for real-time and batch forecasting operations. Deploy models to production environments with monitoring, alerting, and recovery mechanisms. Build automated retraining and evaluation frameworks to ensure model performance and accuracy. Create interactive dashboards and visualizations for stakeholder communication. Collaborate with cross-functional teams including operations, engineering, and business stakeholders. Participate in Agile development processes and contribute to continuous improvement initiatives. Ensure delivery of cost-effective, high-quality forecasting solutions within operational timelines. Required Qualifications Advanced proficiency in Python, including libraries such as pandas, NumPy, scikit-learn, and statsmodels. Strong experience in time-series forecasting techniques such as ARIMA, SARIMAX, or gradient boosting methods. Proven experience deploying machine learning models into production environments with monitoring and maintenance. Experience integrating APIs and external data sources into data pipelines. Working knowledge of AWS services such as EC2, S3, Lambda, or SageMaker. Strong SQL skills with experience handling large datasets and optimizing queries. Experience building dashboards using tools such as Streamlit, Plotly, or similar. Strong understanding of data pipelines, version control (e.g., Git), and MLOps practices. Excellent problem-solving and analytical skills. Strong communication and collaboration skills in cross-functional environments. Preferred Qualifications Experience with advanced feature engineering, uncertainty quantification, and probabilistic forecasting. Domain knowledge in energy markets or renewable generation forecasting. Experience working with Agile tools such as Jira or Confluence. Familiarity with evaluation metrics such as MAE, RMSE, and MAPE.