Lead Data Scientist - Remote

About this role

Requirements

• Expertise building and interpreting machine learning models for regression, classification, clustering, and forecasting in supply chain or manufacturing contexts.
• Proficiency developing and operationalizing solutions with Databricks using Python, SQL, and PySpark for large-scale data processing.
• Experience designing multi-agent AI systems with frameworks such as Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph.
• Strong background applying data science best practices including feature engineering, model validation, performance monitoring, reproducibility, and documentation.
• Ability to partner with cross-functional teams in Supply Chain, Manufacturing Ops, Process Engineering, and Demand Planning to structure ambiguous problems.
• Skill producing executive-ready insights and visualizations using Power BI while ensuring data quality, governance, and model explainability.

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 using regression, classification, clustering, and forecasting to quantify supply chain drivers and improve operational outcomes.
• Develop and operationalize analytics and ML solutions using Databricks with Python, SQL, and PySpark for large-scale data processing and experimentation.
• Design and build multi-agent AI systems including orchestrator-executor architectures, tool-calling agents, and RAG-based decision support using Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph.
• Partner with Supply Chain & Procurement leadership, Manufacturing Ops, Process Engineering, Demand Planning, and IT to translate business problems into structured ML and AI approaches.
• 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.

Benefits

• Tools, training, and opportunities for continuous development and innovation.
• Inclusive culture of growth, collaboration, and accountability.
• Unwavering commitment to safety, partnership, belonging, and impact.