Senior Staff Data Scientist - Consumer Experimentation

About Reddit

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.

Role

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 efficiency and adoption across teams