Lead Data Scientist - US Remote

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—for 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, Auto Gen, Semantic Kernel, or Lang Chain/Lang Graph.
• 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.

• 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.
  • 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 Auto Gen, Semantic Kernel, Lang Chain/Lang Graph, 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.

• Experience operationalizing ML models into production in supply chain or manufacturing environments.
• Familiarity with SAP ECC / S/4
  
  HANA 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…