Senior Data Scientist

Ford Ford · Auto · United States · Global Data Insight & Analytics

Senior Data Scientist at Ford Credit focused on developing and deploying AI/ML solutions for customer experience, risk reduction, and operational efficiency. The role involves building scalable production-ready solutions across conversational AI, fraud detection, forecasting, and intelligent automation, with a strong emphasis on generative AI, LLMs, RAG, and AI agents for workflow automation. Requires experience in Python, ML frameworks, cloud platforms, and MLOps, with a focus on translating business challenges into technical solutions and ensuring responsible AI practices.

What you'd actually do

  1. Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications.
  2. Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications.
  3. Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques.
  4. Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives.
  5. Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques.

Skills

Required

  • Python
  • scikit-learn
  • PyTorch
  • TensorFlow
  • LLMs
  • prompt engineering
  • RAG
  • conversational AI
  • SQL
  • AWS
  • Azure
  • GCP
  • MLOps
  • model deployment
  • model monitoring
  • model versioning
  • CI/CD

Nice to have

  • Master’s degree
  • financial services experience
  • credit risk experience
  • fraud analytics experience
  • regulated industries experience
  • AI agents
  • orchestration frameworks
  • automation platforms
  • model explainability
  • governance tools
  • SHAP
  • LIME
  • software engineering workflows
  • developer productivity tooling
  • mentoring experience
  • leading technical teams

What the JD emphasized

  • 5+ years of experience developing and deploying machine learning or AI solutions in production environments.
  • Experience building predictive models, forecasting solutions, anomaly detection systems, NLP applications, or generative AI solutions.
  • Experience with large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), or conversational AI systems.
  • Understanding of MLOps concepts including model deployment, monitoring, versioning, and CI/CD workflows.
  • Experience in financial services, credit risk, fraud analytics, or regulated industries.

Other signals

  • Develops and deploys AI/ML solutions
  • Focuses on production-ready solutions
  • Works across conversational AI, fraud detection, forecasting, and intelligent automation