Staff Data Scientist

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.

 

• Experience with specific industrial data historians (e.g. Ignition).
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for deploying models at the edge.
• Publications or presentations in the fields of AI, Data Science, or Smart Manufacturing.
• Experience with real-time data streaming architectures.

• Education: Master's or Ph.D. in Data Science, Computer Science, Engineering, Statistics, or a related quantitative field.
• Experience:
  • 8+ years of progressive experience in Data Science, with a significant portion in a leadership or lead contributor role.
  • 5+ years of direct experience applying data science within a manufacturing or industrial environment, ideally automotive.
  • Proven hands-on experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, and practical application in real-world scenarios.
  • Demonstrated understanding and experience working with Operational Technology (OT) data infrastructure, including data sources (PLCs, SCADA, MES), industrial protocols (OPC UA, MQTT), and data flow from factory floor to analytical platforms.
• Technical Expertise:
  • Expert proficiency in Python (Numpy, Pandas, Scikit-learn, TensorFlow/PyTorch) for data manipulation, analysis, and model development.
  • Deep knowledge of LLM architectures and