Tech Stack

Description

You will architect and scale intelligent systems for dark store operations, optimizing the lifecycle of millions of items from supplier to customer in under 20 minutes. Tackle the perishable trade-off by ensuring shelves are stocked while minimizing wastage and maximizing space. Take end-to-end ownership of models that dictate real-world logistics.

Requirements

• 8+ years in Data Science, ideally in E-commerce, Q-commerce, or Supply Chain
• Background in Operations Research or Statistics
• Proficiency in Python, Spark, and ML frameworks (XGBoost, LightGBM, PyTorch)
• Experience with optimization libraries (Gurobi, CPLEX, PuLP) and real-time inference systems
• Strong understanding of experimentation, A/B testing, and causal inference

Responsibilities

• Build and deploy high-precision demand forecasting models at SKU-store level
• Develop replenishment algorithms balancing in-stock availability with expiry risk for perishables
• Use ML to identify items at risk of expiring and trigger automated interventions
• Design and A/B test dynamic pricing strategies to move inventory efficiently
• Optimize dark store space to minimize picker travel time and maximize storage density