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Senior Staff Data Scientist - Consumer Experimentation

As a Senior Staff Data Scientist, you will be the go-to expert on experimentation methodology for Reddit's Consumer team, tackling complex challenges like network effects and influencing product strategy through rigorous experimental design and analysis.

About the job

Responsibilities:

  • 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
  • Influence the long-term product strategy by driving learning through well-designed experiments and translating experimental results into clear, actionable recommendations for senior leadership
  • Mentor and elevate other data scientists across the organization on experimentation best practices, causal reasoning, and statistical rigor
  • Publish and share methodological advances internally and, where appropriate, externally to contribute to the broader experimentation and causal inference community

Required Qualifications:

  • Ph.D. in Statistics, Econometrics, Economics, Computer Science, or a related quantitative field with a strong focus on causal inference or experimentation methodology; or M.S. with equivalent depth of expertise
  • For M.S. holders: 12+ years of industry experience in applied science, data science, or experimentation-focused roles
  • For Ph.D. holders: 8+ years of industry experience in applied science, data science, or experimentation-focused roles
  • Deep expertise in causal inference, including practical experience with challenges such as network interference / spillovers, two-sided experimentation, switchback designs, cluster randomization, and/or synthetic control methods
  • 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
  • Track record of designing and analyzing experiments at scale in complex or networked environments
  • Demonstrated ability to influence product and organizational strategy through experimentation insights
  • Demonstrated ability to take ambiguous, technically complex problems and solve them in a structured, hypothesis-driven way
  • Excellent communication skills with the ability to explain nuanced statistical concepts and tradeoffs to both technical and non-technical senior stakeholders
  • Experience mentoring data scientists and building organizational capability in experimentation and causal reasoning
  • Comfortable in innovative and fast-paced environments with a bias toward action

Preferred Qualifications:

  • Published research or industry contributions in areas such as interference in experiments, network experimentation, or marketplace causal inference
  • Familiarity with Bayesian experimental methods, bandit algorithms, or adaptive experimental designs
  • Experience with social network or user-generated content platforms where community-level dynamics create non-trivial experimentation challenges

Benefits:

  • Comprehensive Healthcare Benefits and Income Replacement Programs
  • 401k with Employer Match
  • 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
  • Flexible Vacation & Paid Volunteer Time Off
  • Generous Paid Parental Leave

Skills

Causal Inference, Experimental Design, SQL, R, Python, Power Analysis, Variance Reduction, Sequential Testing, Multiple Comparison Corrections, Bayesian Experimental Methods

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