Requirements

• Proficiency in Python and PySpark for data analysis, machine learning, and demand forecasting (regression + time series models)
• Experience with forecasting models such as LightGBM/ XGBoost and classical time series methods (e.g., ETS/ARIMA)
• Strong understanding of the machine learning workflow (data cleansing, feature engineering, model evaluation, model explainability)
• Familiarity with forecasting evaluation metrics (e.g. MAPE, MAE) and ability to validate model performance
• Strong SQL skills for managing and querying large datasets
• Knowledge of data visualization tools (e.g., Power BI)
• Experience with cloud-based technologies such as Databricks, Azure, or AWS
• Strong communication skills — able to explain insights and models to both business and technical teams
• Ownership and accountability — able to deliver end-to-end solutions, not just analysis
• Collaboration — able to work effectively with Data Engineering, Tech, Product, and Supply Chain teams

Preferred (Optional) Qualifications:

• Exposure to MLOps tools (e.g., MLflow, job scheduling)
• Experience building production pipelines for forecast outputs (daily/weekly runs) and supporting downstream systems
• Experience in real-time analytics or scheduled processing systems
• Optimization mindset — able to balance accuracy, business impact, and time constraints

Benefits

• Clear focus.
• Diverse Workplace (Our members are from around the world!)
• Non-hierarchical and agile environment
• Growth opportunity and career path