Director, Data Science - AI for Work (ai4w) Ecosystem

Meta Meta · Big Tech · Menlo Park, CA +1 · Remote

Director, Data Science - AI for Work (AI4W) Ecosystem at Meta. This role is a founding senior IC on a new analytics team focused on integrating AI into Meta's products and processes. The role involves shaping product development, measuring impact, strategizing investments, and guiding teams with data-driven insights. Responsibilities include collaborating with cross-functional partners, contributing to technical vision, building and improving AI development/experimentation/measurement systems, and leading data scientists and managers. Requires experience in predictive modeling, ML, experimentation, ethical AI practices, and AI skill development like prompt engineering and agent orchestration.

What you'd actually do

  1. Collaborate with Engineering, Product, and cross-functional teams to inform, influence, support, and execute strategy and investment decisions for Meta.
  2. Contribute to the long-term technical vision and strategy for analytics methods and metrics to enhance the quality and efficiency of our platforms at scale.
  3. Work with engineering and other data scientists to build and improve AI development, automation, experimentation, and measurement methods and metrics, ensuring high-quality throughput and impact.
  4. Develop an understanding of complex, large-scale AI development, experimentation, and measurement systems, as well as broader industry challenges, to identify current and future risks and opportunities.
  5. Inspire, and lead a team of data scientists and managers across multiple teams in close collaboration with other Data Science Directors and Managers.

Skills

Required

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 10+ years of work experience leading analytics work in IC capacity
  • working collaboratively with Engineering and cross-functional partners
  • guiding data-influenced product planning, prioritization and strategy development
  • Experience working effectively with multiple stakeholders and cross-functional teams, including Engineering, PM/TPM, Analytics and Finance
  • Experience framing and communication skills
  • Masters or Ph.D. Degree in a quantitative field
  • Experience with predictive modeling, machine learning, and experimentation/causal inference methods
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration)
  • staying current with emerging AI technologies

What the JD emphasized

  • founding senior IC
  • AI for Work (AI4W) is Meta's company-wide effort to integrate AI into every tool, team, and process at Meta.
  • AI development, automation, experimentation, and measurement methods and metrics
  • large-scale AI development, experimentation, and measurement systems
  • lead a team of data scientists and managers

Other signals

  • AI for Work (AI4W) is Meta's company-wide effort to integrate AI into every tool, team, and process at Meta.
  • shaping product development
  • quantifying new opportunities
  • ensuring products bring value to users and the company
  • guide teams using data-driven insights
  • develop hypotheses
  • employ rigorous analytical approaches
  • tell data-driven stories
  • present clear insights
  • build credibility with stakeholders
  • world-class analytics community
  • skill development and career growth
  • inform, influence, support, and execute strategy and investment decisions
  • Contribute to the long-term technical vision and strategy for analytics methods and metrics
  • enhance the quality and efficiency of our platforms at scale
  • build and improve AI development, automation, experimentation, and measurement methods and metrics
  • ensuring high-quality throughput and impact
  • Develop an understanding of complex, large-scale AI development, experimentation, and measurement systems
  • broader industry challenges
  • identify current and future risks and opportunities
  • Inspire, and lead a team of data scientists and managers
  • quantitative field
  • predictive modeling
  • machine learning
  • experimentation/causal inference methods
  • integrate AI tools to optimize/redesign workflows
  • drive measurable impact (e.g., efficiency gains, quality improvements)
  • responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • ongoing AI skill development (e.g., prompt/context engineering, agent orchestration)
  • staying current with emerging AI technologies