Senior Staff Data Scientist - Consumer Experimentation

in Statistics, Econometrics, Economics, Computer Science, or a related quantitative field with a strong focus on causal inference or experimentation methodology; or M.S.

holders: 12+ years of industry experience in applied science, data science, or experimentation-focused roles

holders: 8+ years of industry experience in applied science, data science, or experimentation-focused roles

Strong theoretical grounding in experimental design, including power analysis, variance reduction techniques, sequential testing, and multiple comparison corrections

Experience with experimentation platforms at scale (e.g., building or significantly extending an internal experimentation platform)

Expert knowledge of SQL and proficiency in R and/or Python for statistical computing

Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support

Family Planning Support

Gender-Affirming Care

Mental Health & Coaching Benefits

Comprehensive Medical Benefits & Health Care Spending Account

Registered Retirement Savings Plan with matching contributions

Serve as the technical authority on experimentation methodology across Consumer, setting standards for design, analysis, and interpretation of experiments in a complex, networked environment

Tackle the hardest experimentation problems at Reddit, including spillover and network effects, interference between treatment and control, two-sided experimentation, and long-run effect estimation

Develop and advance methods for causal inference in settings where standard randomization assumptions are violated, such as cluster-randomized designs, switchback experiments, and synthetic control approaches

Design experimentation frameworks and guardrail metrics that account for ecosystem-level effects, ensuring product teams can measure true causal impact rather than biased local estimates

Identify opportunities where improved experimentation methodology can unlock product insights that were previously unmeasurable or ambiguous

Build and scale self-serve experimentation tools, platforms, and best-practice documentation that increase experimentation velocity and literacy across product, engineering, and design teams