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.