About the role
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
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