Data Scientist

At Ford Motor Company, we believe freedom of movement drives human progress. Creating the future of mobility requires the highly intelligent use of data and AI to drive progress. That’s where you can make an impact as part of our Global Data Insight & Analytics team. We are the trusted advisors that enable Ford to clearly see business conditions, customer needs, and the competitive landscape. With our support, key decision-makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision-making. 

Ford Customer Service Division

, within Ford, is committed to provide our customers the “Always On” experience and our mission is to get our customers back on the road, in the shortest possible time. FCSD is a true-stop shop, offering comprehensive Diagnostics, Service Parts, Repair & Service Capabilities. 

In this role, you will design, build, and deploy advanced statistical, machine learning, and operations research models to solve complex problems in areas such as forecasting, inventory optimization, service parts planning, and logistics/distribution. You will combine deep quantitative expertise with strong software engineering practices to deliver scalable, production-grade analytics solutions on Google Cloud Platform (GCP), and you will partner closely with business stakeholders to translate model insights into measurable operational impact.

• Develop, validate, and deploy advanced models (time series/forecasting, inventory optimization, simulation, and optimization) 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.

• Design and build scalable, modular, and well-tested Python codebases following object-oriented programming and software engineering best practices.

• 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 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.

• Continuously evaluate and improve modeling approaches, incorporating new techniques in statistics, machine learning, and operations research as appropriate.

• PhD in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Economics, or a related quantitative field, OR equivalent experience delivering advanced modeling solutions in supply chain/operations.

• 3+ years of experience (or equivalent) building and applying complex models in supply chain/operations management (e.g., time series modeling, forecasting, inventory optimization, service parts planning, logistics/distribution).

• Demonstrated experience across statistics, machine learning, and operations research (optimization and/or simulation), with rigorous model validation such as backtesting and sensitivity/scenario analysis with respect to impacts on inventory and cost.

• Strong Python programming skills, with demonstrated object-oriented programming and software engineering practices (modular design, testing, version control).

• Strong SQL skills and experience working with large/complex datasets.

• Hands-on experience with Google Cloud Platform, including:

• Cloud Run (deploying containerized services)
• BigQuery and Dataform (data transformations/analytics datasets)
• Cloud Build (CI/CD build/test/deploy pipelines)

• Demonstrated ability to communicate complex model results through clear storytelling and visualizations to technical and non-technical audiences

• Preferred Qualifications: - Deep expertise with scaling approaches for large-scale machine learning, optimization, or simulation problems.

• Direct experience with inventory and service parts planning (e.g., multi-echelon networks, service-level constraints, lead time variability).

• Experience operationalizing models into production workflows (APIs, scheduled pipelines, monitoring/alerting, production support).

• Experience with Docker best practices and production-grade CI/CD and release management on GCP.

• Proven track record influencing stakeholders and driving adoption of analytics products in an operational business environment.