Senior Staff Data Scientist - Consumer Experimentation
Leads experimentation methodology for Reddit’s Consumer organization, tackling causal inference challenges involving network effects, interference, and two-sided systems. The role requires deep statistical expertise, large-scale experimentation experience, and the ability to influence product strategy and mentor data scientists.
About the job
Responsibilities
- Serve as the technical authority on experimentation methodology across Consumer, setting standards for experiment design, analysis, and interpretation in a complex, networked environment.
- Address complex experimentation problems, including spillover and network effects, treatment-control interference, two-sided experimentation, and long-run effect estimation.
- Develop causal-inference methods for settings where standard randomization assumptions are violated, including cluster-randomized designs, switchback experiments, and synthetic control approaches.
- Design experimentation frameworks and guardrail metrics that account for ecosystem-level effects.
- Identify opportunities where improved experimentation methodology can unlock previously unmeasurable or ambiguous product insights.
- Build and scale self-serve experimentation tools, platforms, and best-practice documentation.
- Influence product strategy by translating experimental results into clear recommendations for senior leadership.
- Mentor data scientists on experimentation, causal reasoning, and statistical rigor.
- Publish and share methodological advances internally and externally when appropriate.
Required Qualifications
- Ph.D. in Statistics, Econometrics, Economics, Computer Science, or a related quantitative field focused on causal inference or experimentation methodology; or an M.S. with equivalent expertise.
- M.S. holders: 12+ years of industry experience in applied science, data science, or experimentation-focused roles.
- Ph.D. holders: 8+ years of industry experience in applied science, data science, or experimentation-focused roles.
- Deep expertise in causal inference, including network interference or spillovers, two-sided experimentation, switchback designs, cluster randomization, and/or synthetic control methods.
- Strong grounding in experimental design, including power analysis, variance reduction, sequential testing, and multiple-comparison corrections.
- Experience building or significantly extending experimentation platforms at scale.
- Expert SQL knowledge and proficiency in R and/or Python for statistical computing.
- Experience designing and analyzing experiments at scale in complex or networked environments.
- Ability to influence product and organizational strategy through experimentation insights.
- Ability to solve ambiguous, technically complex problems using a structured, hypothesis-driven approach.
- Excellent communication skills for explaining statistical concepts and tradeoffs to technical and non-technical senior stakeholders.
- Experience mentoring data scientists and building organizational capability in experimentation and causal reasoning.
- Comfort working in innovative, fast-paced environments with a bias toward action.
Preferred Qualifications
- Published research or industry contributions involving experimental interference, 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 dynamics create complex experimentation challenges.
Benefits
- Global benefits supporting workspace, professional development, and caregiving.
- Family planning support.
- Gender-affirming care.
- Mental health and coaching benefits.
- Comprehensive medical benefits and a health care spending account.
- Registered Retirement Savings Plan with matching contributions.
- Income replacement programs.
- Flexible vacation and paid volunteer time off.
- Generous paid parental leave.
Skills
Causal Inference, Experimental Design, SQL, R, Python, Power Analysis, Variance Reduction, Sequential Testing, Synthetic Control, Cluster Randomization, Switchback Experiments, Bayesian Methods, Bandit Algorithms, Adaptive Experimental Design, Experimentation Platforms
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