Reddit

Overview

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, shaping how Reddit learns from its experiments, and influencing product strategy through rigorous experimental design and analysis.

About Reddit

Reddit is a community of communities built on shared interests, passion, and trust, home to the most open and authentic conversations on the internet with over 100,000 active communities and approximately 130 million daily active unique visitors.

Requirements & Eligibility

• 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
• Expert knowledge of SQL and proficiency in R and/or Python for statistical computing

Key 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
• Design experimentation frameworks and guardrail metrics that account for ecosystem-level effects
• Identify opportunities where improved experimentation methodology can unlock product insights
• Build and scale self-serve experimentation tools, platforms, and best-practice documentation
• Influence the long-term product strategy by driving learning through well-designed experiments
• Mentor and elevate other data scientists across the organization on experimentation best practices, causal reasoning, and statistical rigor