Manager, Strategic Projects

Handshake · Enterprise · San Francisco, CA · HAI Delivery Ops

Manager, Strategic Projects leading a team focused on AI data and evaluation work. Responsibilities include managing SPLs, driving project delivery (data pipelines, labeling workflows), translating needs into plans, owning performance metrics, ensuring a good experience for fellows, and partnering with Product/Engineering on tooling. Success involves consistent delivery, improved operational metrics, and strong team leadership. Requires 5+ years in operations, 2+ years managing teams, and experience with complex projects, ideally in AI data operations or ML ops.

What you'd actually do

  1. Leading and managing a team of SPLs across multiple complex AI data and evaluation projects, ensuring clarity of ownership, priorities, and execution.
  2. Driving on time, high quality delivery across custom data pipelines and expert labeling workflows, with a sharp focus on throughput, quality, and cost.
  3. Translating researcher and customer needs into clear project plans, scopes, and operational workflows that SPLs and Fellows can execute.
  4. Owning performance metrics across your portfolio, including quality, throughput, SLAs, utilization, and margin, and using data to drive continuous improvement.
  5. Ensuring a top tier experience for Handshake Fellows, enabling them to produce high quality, scalable data while feeling supported and informed.

Skills

Required

  • 5+ years in operationally intensive roles
  • 2+ years managing high performing teams
  • leading complex, multi stakeholder projects
  • strong operational instincts

Nice to have

  • Prior experience in investment banking, private equity or management consulting
  • Background in AI data operations, ML ops, or technical services

What the JD emphasized

  • AI data and evaluation work
  • high intensity
  • high pressure and fast change
  • operational excellence
  • custom data pipelines
  • expert labeling workflows
  • performance metrics
  • AI data operations
  • ML ops
  • high performing teams
  • execution focused environments
  • complex, multi stakeholder projects
  • AI data operations, ML ops, or technical services
  • high intensity
  • priorities shift quickly
  • getting projects back on track
  • sustained periods of high intensity
  • early, late, and weekend work

Other signals

  • leading a team of SPLs
  • driving on time, high quality delivery
  • translating researcher and customer needs into clear project plans
  • owning performance metrics
  • partnering with Product and Engineering to evolve internal tooling