Title:- Data Scientist / Machine Learning Engineer
Job Summary
We are seeking a Data Scientist / Machine Learning Engineer with strong experience in Retail Supply Chain, Online Fulfillment, and Inventory Optimization. The ideal candidate will develop machine learning and predictive analytics solutions to optimize online order fulfillment, safety stock, inventory planning, demand forecasting, and supply chain performance for a large retail organization.
The candidate should have hands-on experience applying AI/ML techniques to solve complex retail operations problems, improve fulfillment efficiency, reduce stockouts, and optimize inventory levels.
Key Responsibilities
- Design and develop machine learning models to improve online order fulfillment and supply chain operations.
- Build predictive models for demand forecasting, inventory optimization, and safety stock planning.
- Develop algorithms to optimize inventory allocation across stores, warehouses, and fulfillment centers.
- Analyze customer demand patterns, inventory levels, lead times, and replenishment cycles to improve product availability.
- Create data-driven solutions to reduce stockouts, excess inventory, and fulfillment costs.
- Collaborate with Supply Chain, Merchandising, Inventory Planning, Logistics, and Product teams to solve business problems using AI and advanced analytics.
- Build scalable ML pipelines and deploy models into production.
- Monitor model performance and continuously improve forecasting and inventory optimization models.
- Present analytical insights and recommendations to business stakeholders.
Required Skills
- 5+ years of experience as a Data Scientist or Machine Learning Engineer.
- Strong programming skills in Python and SQL.
- Experience with machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.
- Strong knowledge of statistics, predictive modeling, and time-series forecasting.
- Experience working with large-scale retail or supply chain datasets.
- Experience with cloud platforms such as Azure, AWS, or GCP.
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