Data Scientist utilizing Python and PySpark for demand forecasting. Collaborating with multiple teams to deliver end-to-end solutions while handling machine learning tasks.
Responsibilities
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
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.
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