Data Scientist
General Motors
CloudPythonSQLTableau
Staff Data Scientist
Ford Motor Company
full-time
Posted on:
Location Type: Hybrid
Location: Dearborn • Missouri • 🇺🇸 United States
Visit company websiteJob Level
Lead
Tech Stack
CloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
- 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.
- 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.
- 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.
- 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.
- 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.
- Effectively communicate complex analytical findings, project progress, and strategic recommendations to senior leadership and non-technical stakeholders across the organization.
Requirements
- 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 practical application frameworks (e.g., Hugging Face Transformers, LangChain, LlamaIndex).
- Strong SQL skills for complex data extraction and manipulation.
- Familiarity with MLOps principles, CI/CD for ML pipelines, and model monitoring in production.
- Understanding of cloud platforms (GCP) and their relevant data/AI services, particularly for hybrid cloud/edge deployments.
- Leadership & Soft Skills:
- Strong strategic thinking and problem-solving abilities, capable of navigating ambiguity and driving results in a complex environment.
- Excellent verbal and written communication, presentation, and interpersonal skills, with the ability to influence cross-functional teams and senior leadership.
Benefits
- Immediate medical, dental, vision and prescription drug coverage
- Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
- Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
- Vehicle discount program for employees and family members and management leases
- Tuition assistance
- Established and active employee resource groups
- Paid time off for individual and team community service
- A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
- Paid time off and the option to purchase additional vacation time.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data sciencelarge language modelsadvanced statistical modelingmachine learningdeep learningPythonSQLMLOpsdata manipulationmodel development
Soft skills
strategic thinkingproblem-solvingcommunicationpresentationinterpersonal skillsinfluencingcollaborationleadershipnavigating ambiguitydriving results