Data Analytics Engagement Supervisor

We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

In this position...

Ford Marketing Analytics is seeking a highly capable and technically grounded Data Science Manager to lead the Customer Modeling and Lifetime Value team. This LL6 leadership role will manage a team of 4–5 data scientists responsible for developing, deploying, and continuously improving customer propensity, predictive, prescriptive, and Customer Lifetime Value (CLV) models.

This team plays a critical role in enabling more personalized, effective, and measurable marketing activity across the enterprise. The team’s work will support loyalty, retention, upsell, cross-sell, customer engagement, and marketing investment decisions, while helping Ford better understand the full value of its customer relationships.

The Data Science Manager will lead development of Ford’s enterprise CLV framework: a central model and analytical foundation that brings together customer value streams across the company. This capability will help stakeholders understand the customer base, personalize communications and treatments, prioritize investments, and improve decision-making at both customer and portfolio levels.

This individual will combine strong data science and methodological expertise with the ability to translate complex technical work into clear business value. They will partner closely with stakeholders across Marketing, FCSD, Customer Experience, Ford Credit, Integrated Services, and other functions to establish priorities, drive adoption, and integrate modeling outputs into business processes. While this role is primarily focused on North America, it may also support related customer-modeling activities in Europe and other regions.

What you'll do...

• Lead and develop a team of 4–5 data scientists responsible for customer modeling, Customer Lifetime Value, and marketing decision-science capabilities.
• Establish a high-performing, collaborative team culture that emphasizes technical rigor, innovation, accountability, continuous learning, and strong business partnership.
• Coach, mentor, and support the career development and performance management of data scientists on the team.
• Lead the development and enhancement of Ford’s enterprise Customer Lifetime Value modeling framework, including retail and fleet/commercial CLV capabilities.
• Define and guide a portfolio of predictive and prescriptive customer models, including propensity, retention/churn, loyalty, purchase, conquest, service, upsell, cross-sell, next-best-action, and segmentation use cases.
• Drive the use of causal inference, experimentation, marketing measurement, and uplift modeling to evaluate the incremental impact of marketing programs and customer treatments.
• Partner with business stakeholders to translate strategic objectives and ambiguous business questions into clear analytical problems, scalable data science products, and actionable recommendations.
• Help establish the customer-modeling roadmap in partnership with Marketing Analytics leadership, balancing strategic priorities, business needs, technical feasibility, and expected value.
• Ensure the team manages the full model lifecycle, including business problem definition, data sourcing, model development, validation, deployment, performance monitoring, refreshes, documentation, and adoption.
• Provide technical oversight and methodological guidance through model-design reviews, code reviews, validation processes, and data-science best practices.
• Ensure models and analytical products meet high standards for quality, reproducibility, interpretability, performance, privacy compliance, and appropriate governance.
• Partner with technical, product, data engineering, and platform teams to enable scalable deployment and access to CLV and customer-modeling outputs through tools, interfaces, and business workflows.
• Communicate complex modeling concepts, results, limitations, and recommendations clearly to technical…