Senior Staff Data Scientist - Consumer Experimentation

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com.

Reddit is poised to rapidly innovate and grow like no other time in its history. This is a unique opportunity to leave your mark on one of the most influential and trafficked corners of the internet. 

Consumer data science plays a key role in fulfilling Reddit’s mission of bringing community & belonging to the world through deep understanding of how we can better connect people to the best information and communities for them - the heart of Reddit’s product - from crypto to support groups, gaming to AMAs, travel tips to memes.

Reddit's experimentation landscape presents uniquely challenging problems. Our platform is a deeply interconnected network of communities, contributors, and consumers – meaning that standard A/B testing assumptions often break down. We need a senior technical leader who thrives on these hard problems and can raise the bar 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, owning the most complex and high-stakes experimentation challenges across Consumer. You will shape how Reddit learns from its experiments, ensure we draw valid causal conclusions in the presence of network effects and interference, and influence product strategy through rigorous experimental design and analysis.

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  

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