Chief Demand Forecaster

4 days ago

Bengaluru, Karnataka, India beBeeDataModeler Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

Expert Data Modeler Needed

We are seeking an accomplished data modeling professional to create sophisticated models for demand forecasting, customer churn prediction, and inventory optimization.

You will work with large-scale transactional data (e.g., orders, customer behavior) to design robust systems that predict rental demand, identify at-risk customers, and manage inventory efficiently, including handling returns and refurbishments.

Key Responsibilities
  • Demand Forecasting: Develop and implement time-series forecasting models (e.g., using Prophet, ARIMA, or LSTM) to predict rental demand by product (SKU), category, and city.
  • Incorporate features like seasonality, holidays, promotions, and external factors (e.g., weather, economic indicators) to achieve high accuracy.
  • Churn Prediction: Build classification models (e.g., XGBoost, Random Forests) to predict customer churn based on subscription history, order patterns, and behavioral features.
  • Use outputs to inform retention strategies and integrate with inventory models (e.g., estimating returns from churned users).
  • Inventory Management: Design optimization models (e.g., using PuLP or linear programming) to manage stock levels, reorder points, and refurbishment cycles, leveraging demand and churn forecasts to minimize stockouts and overstock costs.
  • End-to-End ML Pipeline: Create data pipelines (ETL) for ingesting and preprocessing order data (e.g., from CSV sources with timestamps, SKUs, cities).
  • Feature engineering: Generate 50-100+ features like lagged orders, customer tenure, day-of-week effects, and holiday flags.
  • Model Deployment & Monitoring: Deploy models as APIs (e.g., using FastAPI, Docker, Kubernetes) for real-time predictions.
  • Implement monitoring for model drift and retraining workflows.
  • Conduct A/B testing and evaluate models using metrics like RMSE (for demand), AUC-ROC (for churn), and cost savings (for inventory).
  • Scalability & Experimentation: Optimize models for large datasets (e.g., millions of orders) using cloud platforms (AWS/GCP).
  • Experiment with advanced techniques like reinforcement learning for dynamic pricing.

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