Meta
Data Scientist (Technical Leadership)
$210K–$281K
Sunnyvale, CA +5 · Full-time · Posted Jun 25, 2026
- Location
- Sunnyvale, CA
Bellevue, WA
Menlo Park, CA
Seattle, WA
New York, NY
San Francisco, CA - Experience
- 8+ years in data science or analytics; the posting also lists a Master's or Ph.D. in a quantitative field, without stating it as an alternative to the experience requirement.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
About the job
Uses data, experimentation, and statistical analysis to shape product strategy, measure product outcomes, and guide investment and roadmap decisions across Meta product teams.
What you'll do
- Analyze complex data and apply quantitative methods, experimentation, and data mining to inform product strategies.
- Set goals, forecast and monitor product metrics, and identify opportunities to improve products.
- Partner with cross-functional teams to influence product strategy, investment decisions, and roadmaps through insights and recommendations.
What they ask for
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- 8+ years of experience in data science or analytics, including leading analytics work in an IC capacity and guiding data-influenced product planning and strategy.
- Experience collaborating with Engineering and cross-functional stakeholders, including PM/TPM, Analytics, and Finance.
- Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact.
Nice to have
- Master's or Ph.D. degree in a quantitative field.
- Experience with predictive modeling, machine learning, experimentation/causal inference, and responsible AI practices.
Pay
$210,000/year to $281,000/year (base salary)
Tools
AI tools
AI focus
Integrate AI tools to optimize or redesign workflows and deliver measurable impact; continue AI skill development, including prompt/context engineering and agent orchestration; apply responsible AI practices such as risk assessment, bias mitigation, and quality and accuracy reviews.
Model-written summary · the original posting is authoritative