Manager, Data Science & AI Strategy

Job Description

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world – together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Do you believe data tells the real story? Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That’s where Global Data Insight & Analytics makes an impact. We advise leadership on 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.

The Data Science Leader is a critical leadership position responsible for shaping and executing Ford’s Data Science and AI strategy within Integrated Services (IS) Analytics. This role will be instrumental in driving the transformation of IS Analytics through advanced data science, machine learning, and AI technologies. The individual will lead end‑to‑end delivery of Data Science and AI products, scale solutions across platforms, and establish best‑in‑class Data Science and AI engineering practices.

As a hands‑on leader, responsibilities include defining and communicating an advanced analytics and AI roadmap, building and mentoring teams, engaging in solution design, model development and deployment, and collaborating with business and technology stakeholders to integrate Data Science and AI into core offerings and deliver measurable outcomes.

• Strategic Leadership & Product Ownership:
  Define and execute the advanced analytics and AI strategy, partnering with business leaders to translate complex challenges into scalable, impactful solutions. Own the product vision and delivery, establishing clear success metrics and driving accountability.
• Hands‑on Technical & Engineering Leadership:
  Provide hands‑on technical guidance in solution design, model development, and deployment. Lead technical and design reviews, direct advanced Data Science and AI research, and build robust engineering practices for architecture, MLOps, and software engineering rigor.
• End‑to‑End Delivery & Scalability:
  Own the end‑to‑end delivery of analytics, AI products and services from concept to production, ensuring rapid iteration, quality, reliability, and scalability in partnership with cross‑functional teams. Influence product direction through data‑driven insights.
• Expert Technical Application:
  Apply deep expertise in cloud‑based AI platforms, modern ML frameworks, Python, containerization, and version control to design, implement, and optimize end‑to‑end ML pipelines and large‑scale data processing for production deployment, including advanced ML algorithms, statistical modeling, and Generative/Agentic AI applications.

• Education:
  
  Master’s Degree in a related field (e.g., Data Science, Predictive Analytics, Statistics, AI, Applied Mathematics, Computer Science).
• Delivery Leadership:
  Seasoned leader in Data Science and AI applications, with proven experience delivering and scaling multiple advanced analytical and AI solutions in production environments.
• Technical Expertise:
  • 3+ years of hands‑on experience with advanced analytics, machine learning, and data science software (e.g., Python‑based tools), with experience in Generative AI or Agentic AI a plus.
  • Experience using cloud platforms (Azure, AWS, or GCP) and enterprise Data Science/AI platforms.
  • Demonstrated proficiency in modern ML frameworks, statistical modeling, and data analysis techniques. Experience with Gen AI technologies is a plus.
• Leadership & Team Development:
  • 3+ years leading and managing direct reports.
  • Proven ability to hire, grow, and mentor high‑performing AI and Data Science teams.

• PhD in a related field (e.g., Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, AI).
• Expertise in advanced and predictive analytical methods (e.g., Simulation, Design of Experiments, Genetic Algorithms, Ensemble Methods,…