# Senior Staff Data Scientist - Consumer Experimentation

**Company:** [Reddit](https://hotfix.jobs/companies/reddit)
**Location:** Remote
**Role:** Data Science
**Experience:** 8+ years
**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
**Posted:** 2026-08-14

> 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.

## Job Description

## 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.

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