Job Responsibilities
• Lead complex data science and machine learning initiatives supporting supply chain, manufacturing operations, capacity planning, demand forecasting, and operational decision-making.
• Design, develop, and own advanced ML solutions - including predictive models, time-series forecasting, optimization, and decision-support systems - scoped to supply chain and manufacturing use cases.
• Build, train, evaluate, and interpret machine learning models (regression, classification, clustering, forecasting) to quantify supply chain drivers, surface optimization opportunities, and improve operational outcomes.
• Develop and operationalize analytics and ML solutions using Databricks (Python / SQL / PySpark) for large-scale data processing, model development, and experimentation.
• Design and build multi-agent AI systems - including orchestrator-executor architectures, tool-calling agents, and RAG-based decision support - using frameworks such as Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph.
• Implement and extend solutions using the MCP to enable AI agents to access and act on enterprise data systems in supply chain and manufacturing contexts.
• Apply data science best practices including feature engineering, model validation, performance monitoring, reproducibility, and documentation.
• Partner with Supply Chain & Procurement leadership, Manufacturing Ops, Process Engineering, Demand Planning, and IT to translate ambiguous business problems into structured ML and AI approaches.
• Develop and maintain self-service, automated, and AI-enabled analytics workflows that reduce manual effort and improve decision latency.
• Leverage Azure AI Foundry, Microsoft Copilot Studio, and Microsoft 365 Copilot extensibility to prototype and deploy AI-powered analytics and agent-based decision-support tools.
• Produce executive-ready insights through clear storytelling, visualizations, and recommendations using Power BI or embedded analytics.
• Set technical direction, establish reusable ML and AI frameworks, and mentor junior and mid-level data scientists across the team.
• Ensure high standards of data quality, governance, model validation, and explainability.
Minimum Qualifications
Education & Experience (one of the following):
• Master's degree in Statistics, Mathematics, Industrial Engineering, Data Science, Computer Science, Engineering, or a related quantitative field with 5+ years of relevant data science/analytics experience, OR
• Bachelor's degree in the same or related fields with 8+ years of relevant data science / analytics experience.
Technical:
• Demonstrated track record delivering advanced ML and data science solutions in supply chain, manufacturing, or industrial domains.
• Strong hands-on experience with machine learning and statistical modeling - development, interpretation, and operational business application.
• Strong proficiency in Databricks (Python, SQL, PySpark, Delta Lake).
• Hands-on experience with the MCP - building or consuming MCP servers/clients to connect AI agents to enterprise data systems, APIs, or ERP modules.
• Hands-on experience with multi-agent system design - architecting multi-agent systems using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service; orchestrator-executor patterns, tool calling, memory management, and agent coordination.
• Compulsory - must have hands-on experience with one or more of the following:
• Azure AI Foundry
• Microsoft Copilot Studio
• Microsoft 365 Copilot extensibility
• Microsoft Power Platform (Power Automate, Power BI)
• Ability to translate complex business problems into ML / AI solutions and communicate findings to both technical and executive audiences.
• Strong stakeholder management and cross-functional collaboration skills.
Preferred Qualifications
• Experience operationalizing ML models into production in supply chain or manufacturing environments.
• Familiarity with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, PM, SD).
• Strong Power BI experience - semantic modeling, performance optimization, executive dashboard design.
• Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics artifacts).
• Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime).
• Experience with statistical / simulation methods (Monte Carlo, scenario analysis, sensitivity analysis) applied to operations and supply chain.
• Familiarity with Palantir Foundry (pipelines, ontology, Workshop, AIP).
• Proven experience mentoring data scientists or leading end-to-end analytics initiatives.
• Familiarity with cloud-native data architectures and governed data platforms.
Other