Senior Staff Data Scientist - Consumer Experimentation

About the Role

Reddit is a community of communities, built on shared interests, passion, and trust. It is home to authentic conversations and is one of the internet’s largest sources of information with over 100,000 active communities and approximately 126 million daily active unique visitors. Reddit is rapidly innovating and growing, presenting a unique opportunity to make a significant impact.

Consumer data science is crucial to Reddit's mission of fostering community and belonging by deeply understanding how to connect people with relevant information and communities. The experimentation landscape at Reddit presents unique challenges due to its deeply interconnected network of communities, contributors, and consumers, which often invalidates standard A/B testing assumptions. We are seeking a senior technical leader who thrives on these complex problems and can elevate the standard for causal inference and experimentation rigor across the entire Consumer organization.

As a Senior Staff Data Scientist on the Consumer team, you will be the go-to expert on experimentation methodology, taking ownership of the most complex and high-stakes experimentation challenges within Consumer. You will play a pivotal role in shaping how Reddit learns from its experiments, ensuring valid causal conclusions despite network effects and interference, and influencing product strategy through rigorous experimental design and analysis.

Responsibilities

• Serve as the technical authority on experimentation methodology across Consumer, establishing standards for design, analysis, and interpretation of experiments in a complex, networked environment.
• Address the most challenging 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 to increase experimentation velocity and literacy across product, engineering, and design teams.
• Influence 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.