Senior Staff Data Scientist – Consumer Experimentation

This is a fully remote position, open to applicants in United States.

📋 Description

• Act as the technical expert on experimentation methodology within the Consumer sector, establishing standards for the design, analysis, and interpretation of experiments in a multifaceted, interconnected environment.

• Address the most challenging experimentation issues at Reddit, such as spillover and network effects, interference between treatment and control groups, two-sided experimentation, and estimating long-term effects.

• Create and enhance methods for causal inference in situations where conventional randomization assumptions do not hold, including cluster-randomized designs, switchback experiments, and synthetic control methods.

• Develop experimentation frameworks and guardrail metrics that consider ecosystem-level impacts, ensuring product teams can accurately measure true causal effects instead of skewed local estimates.

• Discover opportunities where enhanced experimentation methodologies can reveal product insights that were previously unquantifiable or unclear.

• Build and scale self-service experimentation tools, platforms, and best-practice documentation that boost experimentation speed and understanding across product, engineering, and design teams.

• Shape long-term product strategy by promoting learning through well-structured experiments and converting experimental findings into clear, actionable recommendations for senior leadership.

• Mentor and uplift fellow data scientists within the organization regarding best practices in experimentation, causal reasoning, and statistical rigor.

• Publish and disseminate methodological advancements internally and, when appropriate, externally to contribute to the wider experimentation and causal inference community.

⛳️ Requirements

• Ph.D. in Statistics, Econometrics, Economics, Computer Science, or a related quantitative field with a strong emphasis on causal inference or experimentation methodology; or M.S. with equivalent depth of expertise.

• For M.S. degree holders: 12+ years of industry experience in applied science, data science, or roles focused on experimentation.

• For Ph.D. degree holders: 8+ years of industry experience in applied science, data science, or experimentation-focused roles.

• Profound expertise in causal inference, including hands-on experience with challenges such as network interference/spillovers, two-sided experimentation, switchback designs, cluster randomization, and/or synthetic control methods.

• Solid theoretical foundation in experimental design, covering power analysis, variance reduction techniques, sequential testing, and multiple comparison corrections.

• Experience with large-scale experimentation platforms (e.g., developing or significantly enhancing an internal experimentation platform).

• Expert proficiency in SQL and familiarity with R and/or Python for statistical computing.

• Proven history of designing and analyzing experiments at scale within complex or interconnected settings.

• Demonstrated capability to influence product and organizational strategy through insights gained from experimentation.

• Proven ability to tackle ambiguous, technically intricate problems and resolve them in a structured, hypothesis-driven manner.

• Exceptional communication skills with the ability to articulate nuanced statistical concepts and trade-offs to both technical and non-technical senior stakeholders.

• Experience in mentoring data scientists and enhancing organizational capabilities in experimentation and causal reasoning.

• Comfortable in innovative, fast-paced environments with a proactive approach.

🏝️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs.

• 401k with Employer Match.

• Global Benefit programs tailored to your lifestyle, covering workspace, professional development, and caregiving support.

• Family Planning Support.

• Gender-Affirming Care.

• Mental Health & Coaching Benefits.

• Flexible Vacation & Paid Volunteer Time Off.

• Generous Paid Parental Leave.