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
