About this role
Requirements
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Proficiency in Python and PySpark for data analysis, machine learning, and demand forecasting (regression + time series models)
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Experience with forecasting models such as LightGBM/ XGBoost and classical time series methods (e.g., ETS/ARIMA)
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Strong understanding of the machine learning workflow (data cleansing, feature engineering, model evaluation, model explainability)
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Familiarity with forecasting evaluation metrics (e.g. MAPE, MAE) and ability to validate model performance
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Strong SQL skills for managing and querying large datasets
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Knowledge of data visualization tools (e.g., Power BI)
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Experience with cloud-based technologies such as Databricks, Azure, or AWS
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Strong communication skills — able to explain insights and models to both business and technical teams
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Ownership and accountability — able to deliver end-to-end solutions, not just analysis
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Collaboration — able to work effectively with Data Engineering, Tech, Product, and Supply Chain teams
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Preferred (Optional) Qualifications:
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Exposure to MLOps tools (e.g., MLflow, job scheduling)
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Experience building production pipelines for forecast outputs (daily/weekly runs) and supporting downstream systems
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Experience in real-time analytics or scheduled processing systems
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Optimization mindset — able to balance accuracy, business impact, and time constraints
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Benefits
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Clear focus.
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Diverse Workplace (Our members are from around the world!)
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Non-hierarchical and agile environment
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Growth opportunity and career path