Data Analytics Engagement Supervisor

Jobtailor; Dearborn (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Ford is seeking a senior leader to build and guide a 4–5 person data science team focused on customer modeling, LTV, and marketing decision science. You will drive the enterprise modeling framework, establish a high-velocity culture, and partner with Marketing Analytics to translate business questions into scalable data science products.

You will oversee the full lifecycle of models, from data sourcing through deployment and governance, champion responsible AI practices, and communicate results

Qualifications

• Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, Operations Research, Finance, Marketing Analytics, or a related quantitative field
• 5+ years of experience in data science, advanced analytics, customer analytics, marketing analytics, decision science, or a related field
• 2+ years of experience leading technical projects, initiatives, or teams
• Strong experience applying predictive modeling and statistical methods to business problems
• Strong proficiency in Python and SQL
• Visa sponsorship is not available for this position
• Must be legally authorized to work in the United States
• Advanced degree in a listed quantitative field preferred
• Preferred experience includes customer lifetime value modeling, financial modeling, customer economics, profitability analysis, causal inference, uplift modeling, experimentation, marketing optimization, customer modeling, Google Cloud Platform, model deployment, MLOps, model monitoring, production analytics, Generative AI, and relevant industry experience

Responsibilities

• 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 team culture emphasizing technical rigor, innovation, accountability, continuous learning, and business partnership
• Coach, mentor, and support career development and performance management of data scientists
• Lead development and enhancement of Ford’s enterprise Customer Lifetime Value modeling framework
• Define and guide predictive and prescriptive customer models for propensity, retention/churn, loyalty, purchase, conquest, service, upsell, cross-sell, next-best-action, and segmentation use cases
• Drive causal inference, experimentation, marketing measurement, and uplift modeling to evaluate marketing programs and customer treatments
• Translate strategic objectives and ambiguous business questions into analytical problems, scalable data science products, and actionable recommendations
• Establish the customer-modeling roadmap with Marketing Analytics leadership
• Oversee the full model lifecycle, including data sourcing, development, validation, deployment, monitoring, refreshes, documentation, and adoption
• Provide technical oversight through model-design reviews, code reviews, validation processes, and data-science best practices
• Ensure analytical products meet standards for quality, reproducibility, interpretability, performance, privacy compliance, and governance
• Partner with technical, product, data engineering, and platform teams on scalable deployment and access to modeling outputs
• Communicate modeling concepts, results, limitations, and recommendations to technical and non-technical audiences
• Build stakeholder relationships across Marketing, FCSD, CX, Ford Credit, Integrated Services, and other enterprise partners
• Drive continuous improvement using emerging methods, tools, and responsible Generative AI applications
• Implement “Close the Loop,” OKR, and Performance+ objectives and processes within Marketing Analytics

Skills

Data Science Leadership

Predictive Modeling

Python

SQL

Team Leadership

Marketing Analytics

Causal Inference

Uplift Modeling

Experimentation

MLOps

Education

Bachelor's degree in a quantitative field

Advanced degree preferred

Tools

Google Cloud Platform

MLOps

Model Deployment

Production Analytics

Generative AI

Job description

• 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 team culture emphasizing technical rigor, innovation, accountability, continuous learning, and business partnership
• Coach, mentor, and support career development and performance management of data scientists
• Lead development and enhancement of Ford’s enterprise Customer Lifetime Value modeling framework
• Define and guide predictive and prescriptive customer models for propensity, retention/churn, loyalty, purchase, conquest, service, upsell, cross-sell, next-best-action, and segmentation use cases
• Drive causal inference, experimentation, marketing measurement, and uplift modeling to evaluate marketing programs and customer treatments
• Translate strategic objectives and ambiguous business questions into analytical problems, scalable data science products, and actionable recommendations
• Establish the customer-modeling roadmap with Marketing Analytics leadership
• Oversee the full model lifecycle, including data sourcing, development, validation, deployment, monitoring, refreshes, documentation, and adoption
• Provide technical oversight through model-design reviews, code reviews, validation processes, and data-science best practices
• Ensure analytical products meet standards for quality, reproducibility, interpretability, performance, privacy compliance, and governance
• Partner with technical, product, data engineering, and platform teams on scalable deployment and access to modeling outputs
• Communicate modeling concepts, results, limitations, and recommendations to technical and non-technical audiences
• Build stakeholder relationships across Marketing, FCSD, CX, Ford Credit, Integrated Services, and other enterprise partners
• Drive continuous improvement using emerging methods, tools, and responsible Generative AI applications
• Implement “Close the Loop,” OKR, and Performance+ objectives and processes within Marketing Analytics

Requirements

• Bachelor’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, Operations Research, Finance, Marketing Analytics, or a related quantitative field
• 5+ years of experience in data science, advanced analytics, customer analytics, marketing analytics, decision science, or a related field
• 2+ years of experience leading technical projects, initiatives, or teams
• Strong experience applying predictive modeling and statistical methods to business problems
• Strong proficiency in Python and SQL
• Visa sponsorship is not available for this position
• Must be legally authorized to work in the United States
• Advanced degree in a listed quantitative field preferred
• Preferred experience includes customer lifetime value modeling, financial modeling, customer economics, profitability analysis, causal inference, uplift modeling, experimentation, marketing optimization, customer modeling, Google Cloud Platform, model deployment, MLOps, model monitoring, production analytics, Generative AI, and relevant industry experience

Core Competencies

Demonstrates expertise in leading data science teams and developing customer modeling frameworks, with a strong focus on predictive modeling, statistical methods, and marketing analytics. Proven ability to translate business objectives into actionable data science products while ensuring quality and compliance.

Highest-signal resume keywords

• Customer Lifetime Value Modeling
• Predictive Modeling
• Python Proficiency
• SQL Proficiency
• Data Science Leadership

ATS Optimization Keywords

Hard Skills

• Predictive Modeling
• Statistical Methods
• Customer Analytics
• Advanced Analytics
• Causal Inference
• Uplift Modeling
• Experimentation
• Marketing Optimization
• Financial Modeling
• Customer Economics

Soft Skills

• Coaching
• Mentoring
• Communication
• Team Development
• Stakeholder Relationship Building

Industry Keywords

• Data Science
• Marketing Analytics
• Decision Science
• Customer Modeling
• Performance Management

Tools & Technologies

• Google Cloud Platform
• MLOps
• Model Deployment
• Production Analytics
• Generative AI