Snap
Lead Data Scientist, Core, Level 6
$195K–$343K
Los Angeles, California +5 · Full-time · Posted Sep 25, 2026
- Location
- Los Angeles, California
Seattle, Washington
San Francisco, California
Palo Alto, California
New York, New York
Bellevue, Washington - Experience
- 7+ years post-Bachelor’s; or Master’s + 6+ years post-grad; or PhD + 3+ years post-grad in quantitative analysis and/or data science.
- Education
- Bachelor's Degree in a relevant technical or quantitative field or equivalent years of practical work experience.
About the job
Leads data science and analytics projects, applying experimentation, causal inference, statistical methods, and machine learning to guide product strategy and organizational decisions.
What you'll do
- Lead experimentation and causal inference initiatives and cross-functional projects.
- Build and maintain scalable statistics, machine learning, and data products; conduct analyses and deliver insights that inform strategy and decision-making.
- Drive the analytics roadmap, measurement, and insights sharing while collaborating with product, engineering, and research teams.
What they ask for
- Bachelor’s degree in a relevant technical or quantitative field or equivalent years of practical work experience.
- Meet one of these experience paths: 7+ years post-Bachelor’s in quantitative analysis and/or data science; Master’s degree in a relevant field plus 6+ years post-grad; or PhD in a related field plus 3+ years post-grad.
- Expertise in SQL and advanced proficiency with Python or R.
- Strong knowledge of predictive modeling, machine learning, experimentation, and causal inference methods.
Nice to have
- Advanced degree in a quantitative or technical field.
- Experience leading data capture, measurement, and optimization; cross-functional partnerships; or causal inference and A/B testing at scale.
Pay
$229,000-$343,000 annually (USD, base salary); $218,000-$326,000 annually (USD, base salary); $195,000-$292,000 annually (USD, base salary)
Tools
Python, R, SQL, SciPy, stats models, EconML
AI focus
Use AI tools to develop scalable data solutions more efficiently while maintaining accuracy and statistical integrity; apply evolving AI systems and tools to data-driven decision-making and insights generation.
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