Data Scientist, Analytics (technical Leadership)

Meta Meta · Big Tech · Bellevue, WA +2

This role is for a Data Scientist focused on analytics and product strategy, collaborating with cross-functional teams to drive product development and user value. While the role involves using AI tools and adhering to ethical AI practices, its core function is not building AI models but rather leveraging data and analytics to inform product decisions and strategy.

What you'd actually do

  1. Work with complex data sets to solve challenging problems using analytical and statistical approaches
  2. Apply technical expertise in quantitative analysis, experimentation, and data mining to develop product strategies
  3. Identify and measure success through goal setting, forecasting, and monitoring key metrics
  4. Partner with cross-functional teams to inform and execute product strategy and investment decisions
  5. Build long-term vision and strategy for programs and products

Skills

Required

  • SQL
  • Python
  • R
  • SAS
  • Matlab
  • predictive modeling
  • machine learning
  • experimentation/causal inference methods
  • communication of complex technical topics
  • prompt/context engineering
  • agent orchestration

Nice to have

  • Masters or Ph.D. Degree in a quantitative field

What the JD emphasized

  • 5+ years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, SAS, Matlab)
  • 8+ years of work experience leading analytics work in IC capacity, working collaboratively with Engineering and cross-functional partners, and guiding data-influenced product planning, prioritization and strategy development
  • 10+ years of experience communicating the results of analyses to leadership teams to influence the strategy
  • 10+ years of experience doing complex quantitative analysis in product analytics
  • 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) and staying current with emerging AI technologies