Data Scientist

Ford Ford · Auto · Dearborn, MI +1 · Global Data Insight & Analytics

The Data Scientist role at Ford focuses on leveraging Generative AI, Agentic Workflows, Machine Learning, and Statistics to drive evidence-based decision-making across various business domains. The position involves designing and implementing ML models, developing RAG pipelines, building AI agents with orchestration frameworks, and implementing safety layers and monitoring for these systems. The role requires experience with GCP and Python for AI development.

What you'd actually do

  1. Design and implement supervised and unsupervised machine learning models (e.g., regression, classification, clustering) and statistical experiments to support data-driven decision-making.
  2. Develop advanced RAG (Retrieval-Augmented Generation) pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical documents, and external third-party data.
  3. Implement robust safety layers and input/output validation using specialized frameworks (e.g., Google Model Armor) to prevent hallucinations, ensure data privacy, and maintain compliance.
  4. Design and build state-of-the-art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK) to automate complex reasoning tasks.
  5. Build comprehensive monitoring pipelines using advanced evaluation tools (e.g., Arize Phoenix, Langfuse) to trace agent reasoning steps, track token usage, and monitor latency in production.

Skills

Required

  • Bachelor’s degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or Engineering) or equivalent combination of relevant education and experience.
  • 3 + years of professional experience in Data Science, with a strong dual focus on Generative AI/LLM applications and traditional Machine Learning.
  • 2+ years of hands-on experience with supervised/unsupervised learning and statistical modeling.
  • 1+ years of experience building Agentic workflows (reasoning loops, tool use/function calling) rather than simple chatbots.
  • 1+ years of experience with the GCP stack for AI/Data (Vertex AI, BigQuery).

Nice to have

  • Master’s or PhD in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or Engineering).
  • Advanced proficiency in Python libraries such as Scikit-learn, NumPy, Pandas, Matplotlib, TensorFlow, or PyTorch.
  • Skilled in integrating diverse data sources via SQL, Vector Databases, and APIs. Experience with data augmentation and efficient loading techniques.
  • Expert proficiency in Python for building autonomous agents and model interaction (e.g., LangGraph, LangChain, Google ADK).
  • Proficiency in observability frameworks, guardrail implementation, and the Model Context Protocol (MCP).
  • Deep experience with GCP services, specifically Vertex AI, Cloud Run, and BigQuery for deploying scalable AI solutions.
  • Ability to decompose complex business challenges into executable AI agent workflows and technical specifications.
  • Excellent verbal and written communication skills, with a demonstrated ability to translate complex technical information into simple, understandable language for non-technical audiences.
  • Strong skills in building relationships and collaborating effectively with stakeholders to contribute to data-driven decision-making.
  • Highly effective in working with other technical experts, Software Engineers. Product Managers, Data Engineers, and business stakeholders

What the JD emphasized

  • Agentic Workflows
  • AI agents
  • guardrails
  • Agentic workflows (reasoning loops, tool use/function calling) rather than simple chatbots

Other signals

  • Generative AI
  • Agentic Workflows
  • Machine Learning
  • LLMs
  • RAG
  • AI agents