Senior Staff Data Scientist - Consumer Relevance

About the 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 relevance challenges are uniquely complex. Our platform is a deeply interconnected network of communities, contributors, and consumers - where the notion of "relevance" spans personalized content ranking, community discovery, and search across an enormous corpus of authentic, user-generated content. We need a senior technical leader who thrives on these hard problems and can raise the bar for how we measure, evaluate, and improve the quality of recommendations and search results across the entire Consumer organization.

As a Senior Staff Data Scientist on the Consumer team, you will be the go-to expert on relevance measurement and evaluation, partnering closely with Feeds and Search ML teams to tackle the most complex ranking, recommendation, and retrieval challenges across Consumer. You will shape how Reddit understands content quality, define the metrics and analytical frameworks that guide relevance improvements, and influence product strategy through rigorous analysis and experimentation.

Responsibilities

• Serve as the technical authority on relevance metrics and evaluation methodology across Consumer, setting standards for how we measure the quality of feeds, search results, and recommendations in a complex, community-driven environment
• Develop metrics frameworks and offline evaluation approaches for ranking and recommendation systems, including proxy metrics that reliably predict long-term outcomes like retention, community health, and user satisfaction
• Design and analyze experiments for relevance features, accounting for challenges unique to networked platforms such as spillover effects between communities, interference between contributors and consumers, and long-run impacts of ranking changes on content supply
• Identify opportunities where improved measurement and analysis can unlock product insights that were previously unmeasurable or ambiguous, particularly around content quality, search intent understanding, and personalization effectiveness
• Partner deeply with ML engineers and product teams to translate model performance metrics into user-facing impact
• Influence the long-term product strategy for Feeds and Search by synthesizing insights from experimentation, observational analysis, and metric deep-dives into clear, actionable recommendations for senior leadership
• Mentor and elevate other data scientists across the organization on relevance evaluation, experimentation best practices for ranking systems, causal reasoning, and statistical rigor
• Publish and share methodological advances internally and, where appropriate, externally to contribute to the broader relevance, recommendation systems, and experimentation community

Required Qualifications

• Ph.D. in Statistics, Computer Science, Information Retrieval, Economics, or a related quantitative field with a strong focus on recommendation systems, ranking, causal inference, or evaluation methodology; or M.S. with equivalent depth of expertise
• For M.S. holders: 12+ years of industry experience in applied science, data science, or relevance/ranking-focused roles
• For Ph.D. holders: 8+ years of industry experience in applied science, data science, or relevance/ranking-focused roles
• Deep expertise in metrics design and evaluation for ranking and recommendation systems, including offline metrics and counterfactual evaluation
• Strong understanding of causal inference and experimentation methodology, including practical experience with challenges relevant to ranking systems such as novelty effects, position bias, long-run effect estimation, and ecosystem-level impacts
• Experience defining and validating quality metrics for content ranking, search, or recommendations at scale
• Strong theoretical grounding in experimental design, including power analysis, variance reduction techniques, and sequential testing as applied to relevance experiments
• Expert knowledge of SQL and proficiency in R and/or Python for statistical computing
• Demonstrated ability to influence product and organizational strategy through data-driven insights about content quality and user experience
• Excellent communication skills with the ability to explain nuanced statistical and ML concepts and tradeoffs to both technical and non-technical senior stakeholders
• Experience mentoring data scientists and building organizational capability in relevance evaluation and experimentation
• Comfortable in innovative and fast-paced environments with a bias toward action

Preferred Qualifications

• Published research or industry contributions in areas recommendation systems or causal inference for ranking
• Experience with social network or user-generated content platforms where community-level dynamics create non-trivial relevance and experimentation challenges