Data Scientist

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

Required Skills & Experience

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

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)