Senior Staff Data Scientist - Consumer Relevance
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Compensation
Salary & market context
211% above the BLS national median
BLS national median: $74,680
- $325,500
- In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission.
Requirements
Top requirements
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- 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
Perks & setup
Benefits candidates care about
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- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
Why candidates care
Benefits & perks
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- Comprehensive Healthcare Benefits and Income Replacement Programs
- 401k with Employer Match
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Flexible Vacation & Paid Volunteer Time Off
Start here
Requirements
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- 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
Responsibilities
What you'll do
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- 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
Role snapshot
About the role
Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .
Reddit is poised to rapidly innovate and grow like no other time in its history. This is a unique opportunity to leave your mark on one of the most influential and trafficked corners of the internet.
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.
More detail
Nice to have
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- 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
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