Description

Lead the architecture and delivery of the intelligence layer for a Warehouse Intelligence Platform, transforming raw operational data into real-time, actionable decisions.

Responsibilities

• Design and deploy end-to-end ML/AI architectures that integrate with warehouse execution platforms to solve complex logistics problems like wave management and labor balancing.
• Own the end-to-end build and demonstration of customer-facing AI proof-of-concepts, from data exploration through model validation to stakeholder presentation.
• Build and refine optimization models for order batching, pathfinding, and task interleaving to minimize travel time and maximize throughput.
• Develop forecasting models for warehouse volume, labor requirements, and equipment maintenance to prevent operational bottlenecks.
• Establish MLOps best practices for model deployment, monitoring, and re-training to ensure systems adapt to seasonal demands.

Required Skills

• 5+ years in applied data science with production AI and ML.
• Strong proficiency in Python (pandas, Pytorch, TensorFlow, scikit-learn) and SQL.
• Experience with Databricks ML, Delta Lake, and MLflow for model lifecycle management.
• Demonstrated experience with production classification, forecasting, and anomaly detection algorithms (e.g., XGBoost, Random Forest, ARIMA/Prophet, LSTM).
• Ability to work with event-driven data including timestamps, state transitions, and logs.
• Experience integrating LLMs and Agentic workflows for operational intelligence.
• Strong problem-solving and analytical skills with the ability to translate business requirements into technical specifications.

Key Skills

Education

Any Graduate