Ford Staff Data Scientist 

United States, California, Palo Alto 

939705025

As the Lead Data Scientist for Manufacturing AI & OT Data Strategy, you will play a multifaceted role, combining leadership, strategic thinking, and hands-on technical expertise:

1. Strategic Leadership:
   • Define the strategic roadmap for applying data science, particularly LLMs and advanced analytics, to critical manufacturing challenges.
   • Oversee the end-to-end lifecycle of data science projects, from problem definition and data acquisition to model development, deployment, and continuous monitoring.
2. Manufacturing Domain Expertise & Problem Solving:
   • Collaborate deeply with manufacturing operations, engineering, quality, and supply chain teams to identify high-impact problems solvable through data science and AI.
   • Translate complex manufacturing challenges (e.g., predictive maintenance, quality defect prediction, process optimization, root cause analysis, production scheduling) into actionable data science initiatives.
   • Apply a wide range of data science techniques, including advanced statistical modeling, machine learning, and deep learning, to deliver robust and scalable solutions.
3. LLM Application & Innovation:
   • Drive the exploration and implementation of Large Language Models (LLMs) to unlock insights from unstructured manufacturing data (e.g., maintenance logs, quality reports, operator notes, safety incident reports, technical documentation).
   • Lead initiatives in prompt engineering, fine-tuning LLMs for manufacturing-specific tasks, and developing Retrieval Augmented Generation (RAG) systems to enhance knowledge retrieval and decision support.
   • Identify opportunities for generative AI to automate reporting, summarize complex data, or assist in troubleshooting.
4. OT Data Infrastructure & Integration Strategy:
   • Serve as a key liaison and strategic partner with OT Engineering and Production IT teams. Understand the architecture and capabilities of our OT data infrastructure (PLCs, SCADA, MES, industrial sensors, historians, industrial networks).
   • Influence and guide the strategy for collecting, structuring, and accessing high-quality, real-time data from OT systems to ensure it meets the demands of advanced analytics and AI models.
   • Identify and advocate for necessary improvements or expansions in OT data pipelines, edge computing capabilities, and data governance to support AI initiatives.
5. Solution Deployment & MLOps:
   • Work closely with ML Engineers and Data Engineers to ensure seamless deployment, integration, and monitoring of data science models (including LLMs) into production environments, potentially at the edge.
   • Champion MLOps best practices to ensure model reliability, scalability, and maintainability.
6. Communication & Stakeholder Management:
   • Effectively communicate complex analytical findings, project progress, and strategic recommendations to senior leadership and non-technical stakeholders across the organization.
   • Build strong relationships and influence decision-making through compelling data storytelling and business acumen.