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