Supply Chain Data Scientist (Optimization & Forecasting)

Senior Supply Chain Data Scientist (On-Site)

Location: Analytics Center / Office (Full-Time, 100% In-Office)

Experience: 4–8 Years

Department: Advanced Analytics & Operations Research

About The Role

We are looking for a quantitative Supply Chain Data Scientist to build the mathematical engines that power our predictive logistics, network optimization, and automated inventory management platforms. Working hand-in-hand with our data engineers, software architects, and client success leads at our central office, you will transform massive, multi-enterprise logistics datasets into actionable, real-time decision frameworks that help global enterprises minimize stock-outs, reduce holding costs, and bulletproof their supply chains against disruption.

Key Responsibilities & Detailed Breakdown:1. Demand Forecasting & Predictive Modeling

• Design, train, and deploy advanced machine learning models (gradient boosting, deep learning time-series architectures, and probabilistic estimators) for multi-horizon demand forecasting and SKU-level volatility analysis.
• Build predictive models to anticipate supplier lead-time fluctuations, customs clearance bottlenecks, and transportation carrier delays before they impact downstream production schedules.
• Continuously monitor model performance, analyze feature drift, and retrain pipelines using automated data feeds from enterprise ERP and WMS integrations.
• Operations Research & Network Optimization
• Formulate and solve complex operations research (OR) optimization problems, including multi-echelon inventory optimization, safety stock placement, and network footprint design.
• Develop mathematical algorithms for dynamic vehicle routing, last-mile delivery scheduling, and freight consolidation using optimization solvers (e.g., Gurobi, PuLP, or SciPy optimization libraries).
• Build digital twin simulation models of physical supply chain networks to test demand shocks, supplier failures, and inventory rebalancing strategies virtually before production deployment.
• Productionizing Analytics & Feature Engineering
• Collaborate closely with Data Engineers to design efficient feature stores and data transformation pipelines in Python and SQL that feed high-frequency scoring engines.
• Translate experimental Jupyter notebook models into clean, modular, and containerized microservices ready for enterprise-grade deployment.
• Optimize algorithmic query execution and vector/matrix calculations to ensure sub-second response times for interactive user dashboards.
• Cross-Functional Collaboration & Client Workshops
• Engage in daily face-to-face whiteboarding and sprint planning sessions with software engineering squads to align model outputs with core product features.
• Partner with product managers and client-facing teams to interpret analytical findings, validate assumptions against real-world logistics constraints, and present optimization insights directly to enterprise clients during on-site consultations.
  
  

Required Skills & Qualifications

• Work Mode: 100% On-site commitment with daily physical attendance at our office and analytics center.
• Core Technical Stack:
• Expert-level Python (Pandas, NumPy, Scikit-Learn, PyTorch/TensorFlow, Statsmodels).
• Strong mastery of relational databases and complex SQL querying.
• Experience with Operations Research solvers (Gurobi, CPLEX, or SciPy optimization).
• Familiarity with containerization tools (Docker) and version control (Git).
• Domain Expertise: Deep practical knowledge of core supply chain metrics and concepts (Fill Rate, Days Sales of Inventory, Inventory Turns, Bullwhip Effect, and multi-echelon inventory routing).
• Education & Background: Advanced degree (Master's or Ph.D.) in Operations Research, Industrial Engineering, Data Science, Applied Mathematics, or a related quantitative field.
  

Why Join Us On-Site

Collaborating side-by-side with domain experts and software teams in a physical office setting enables rapid mathematical whiteboarding, immediate feedback loops on model constraints, and direct participation in high-stakes enterprise analytics implementations.

Skills: pandas,scipy,gurobi,supply chain,numpy,statsmodels,docker,tensorflow,cplex,analytics