Sr. Director, AI Data Acquisition and Operations

Microsoft Microsoft · Big Tech · Mountain View, CA +1 · Business Strategy

This role leads AI data acquisition and operations for frontier models, focusing on sourcing, negotiating, and delivering training data. It involves managing vendor programs, structuring novel data deals, and building scalable operational systems for data validation and delivery.

What you'd actually do

  1. Partner with researchers to initiate and monitor critical pre- and post-training data collections, including vendor selection, contract negotiation, and management.
  2. Partner with and oversee data operations for AI training data, including setting Microsoft's validation workflows, delivery tracking, and ensuring alignment with research goals.
  3. Oversee a system for procurement processes, including tracking wallets, vendor spend, and milestone delivery.
  4. Partner to help govern a centralized data catalog for validated datasets, metadata, and storage locations to support compliance and discoverability.
  5. Coordinate cross-functional initiatives across Business Development, Procurement, Legal, Compliance, Finance, and Engineering.

Skills

Required

  • Bachelor's Degree AND 8+ years experience in strategy or finance, data operations, program/technical program management, procurement, or finance integration OR equivalent experience.
  • 2+ years of management experience in overseeing large-scale data operations for leading AI research organizations or leading vendors.
  • Experience building and leading a team.
  • Track record building operational systems — spend tracking, delivery milestones, validation workflows — that scale beyond manual effort.
  • Experience operating in a research environment where priorities shift quickly and requirements are underspecified.

Nice to have

  • Bachelor's Degree AND 10+ years experience in technical data operations, procurement program management, or finance systems integration OR equivalent experience.
  • 3+ years of management experience in overseeing large-scale data operations for leading AI research organizations or leading vendors.
  • Fluency with data rights and licensing terms, and comfort working with legal and privacy partners on novel questions.

What the JD emphasized

  • building and leading a team
  • Track record building operational systems — spend tracking, delivery milestones, validation workflows — that scale beyond manual effort
  • operating in a research environment where priorities shift quickly and requirements are underspecified

Other signals

  • sourcing training data
  • negotiating data deals
  • managing data operations
  • building scalable systems