Finance AI & Data Transformation Leader

Ford

Dearborn (MI)

On-site

USD 180,000 - 280,000

Full time

4 days ago
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Job summary

Ford seeks a senior leader to define and execute the Finance data, analytics, and AI strategy within the Global Data, Insights & Analytics (GDIA) team. You will partner with the FOX Finance Transformation team and ERP tech groups to build data infrastructure and governance, delivering measurable business impact.

You will recruit and mentor a world-class team of data scientists, AI engineers, and product managers, ensuring responsible AI practices and strong data quality across programs.

Qualifications

  • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field.
  • 10+ years of progressive experience in analytics, data science or AI roles.
  • 6+ years in a leadership role building and managing data/AI teams.
  • Experience supporting corporate finance functions (FP&A, Accounting, Tax, Audit, Treasury).
  • Expertise in statistical modeling, ML algorithms, generative AI and their business applications.

Responsibilities

  • Define and execute the data/AI strategy for Finance within GDIA.
  • Lead end-to-end lifecycle of AI/ML solutions from ideation to deployment and monitoring.
  • Recruit, mentor, and develop a high-performing data/AI team.
  • Collaborate with cross-functional teams (AIAC, DPE, ERP) and Finance stakeholders.

Skills

Strategic leadership
Data science
AI leadership
Communication

Education

Bachelor's degree in a quantitative field

Tools

Python
R
SQL

Job description

Ford seeks a senior leader to define and execute the Finance data, analytics, and AI strategy within the Global Data, Insights & Analytics (GDIA) team. You will partner with the FOX Finance Transformation team and ERP tech groups to build data infrastructure and governance, delivering measurable business impact.

You will recruit and mentor a world-class team of data scientists, AI engineers, and product managers, ensuring responsible AI practices and strong data quality across programs.

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