Data Scientist
Posted Updated
The Supply Chain Analytics division withing Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified Data Scientists who can develop scalable solutions to complex real-world problems using Machine Learning, AI and Cloud Solutions.
- Develop, validate, and deploy advanced Machine Learning Models & Agentic solutions to support supply chain and operations decision-making.
- Apply rigorous model validation techniques, including backtesting, sensitivity analysis, and scenario analysis, to quantify impacts on inventory and cost.
- Write efficient SQL to query, transform, and analyze large and complex datasets.
- Build and deploy containerized services and data pipelines on Google Cloud Platform, leveraging Cloud Run, BigQuery, Dataform, and Cloud Build.
- Operationalize models into production workflows, including APIs, scheduled pipelines, and monitoring/alerting for ongoing production support.
- Communicate complex analytical findings clearly through storytelling, visualizations, and presentations tailored to both technical and non-technical audiences.
- Partner with cross-functional stakeholders across supply chain, operations, and engineering teams to drive adoption of analytics products and influence business decisions.
- Education: Bachelor’s degree in computer science, Data Science, Statistics, or a related technical field.
- Experience: 3+ years of professional experience in AI/ML engineering or Software Engineering with a focus on model deployment.
- GenAI Stack: Hands-on experience with LLMs and orchestration frameworks (e.g., LangChain, Langgraph)
- GCP Proficiency: Working knowledge of Google Cloud Platform, specifically Vertex AI, BigQuery, and Cloud Run.
Core ML Knowledge: Strong foundation in machine learning theory and practical experience with libraries such as scikit-learn, XGBoost, or PyTorch/TensorFlow.
- Software Engineering: Proficiency in Python and a solid understanding of software best practices, including API design, Git version control, and unit testing.