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What you'll do

• Develop high-impact ML solutions for logistics problems like ETA prediction and demand forecasting.
• Own the end-to-end ML lifecycle from problem definition to model deployment and monitoring.
• Build scalable ML systems with robust data pipelines and feature stores for real-time inference.
• Define success metrics and rigorously evaluate model performance to improve key business KPIs.
• Provide technical leadership, establish best practices in ML, and mentor engineers across the team.

What we're looking for

• 5+ years of experience building and deploying production-grade machine learning systems.
• Strong proficiency in Python and hands-on experience with modern ML frameworks like PyTorch or TensorFlow.
• Solid understanding of statistics, feature engineering, model evaluation methodologies, and experimental design.
• Experience deploying, monitoring, and maintaining ML models in production environments.
• Background in logistics, marketplace systems, forecasting, optimization, recommendation systems, or time-series modeling.
• Familiarity with MLOps tooling such as Airflow, Kubeflow, MLflow, feature stores, and CI/CD pipelines for ML workflows.