Senior Staff Data Scientist - Consumer Experimentation

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