Senior Staff Data Scientist, Consumer Relevance
New
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Reddit, Inc.Data science
Remote - United StatesFull-TimeStaff
Salary$232,500 — $325,500 USD
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Job Details
- Experience
- 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
- Required Skills
- PythonSQLR
Requirements
- Hold a Ph.D. in Statistics, Computer Science, Information Retrieval, Economics, or a related quantitative field focused on recommendation systems, ranking, causal inference, or evaluation methodology; alternatively, hold an M.S. with equivalent depth of expertise.
- Have 12+ years of relevant industry experience with an M.S., or 8+ years with a Ph.D.
- Bring deep expertise in metrics design and evaluation for ranking and recommendation systems, including offline metrics and counterfactual evaluation.
- Understand causal inference and experimentation methodology, including novelty effects, position bias, long-run effect estimation, and ecosystem-level impacts.
- Have experience defining and validating quality metrics for content ranking, search, or recommendations at scale.
- Have a strong grounding in experimental design, including power analysis, variance reduction, and sequential testing for relevance experiments.
- Have expert knowledge of SQL.
- Be proficient in R and/or Python for statistical computing.
- Demonstrate the ability to influence product and organizational strategy through data-driven insights about content quality and user experience.
- Have experience mentoring data scientists and building organizational capability in relevance evaluation and experimentation.
Responsibilities
- Set standards for measuring the quality of feeds, search results, and recommendations across Consumer.
- Develop metrics frameworks and offline evaluation approaches for ranking and recommendation systems, including proxy metrics for long-term outcomes.
- Design and analyze relevance experiments, accounting for spillover effects, interference, and long-run impacts on content supply.
- Identify opportunities to improve measurement of content quality, search intent, and personalization effectiveness.
- Partner with ML engineers and product teams to translate model performance metrics into user-facing impact.
- Synthesize experimentation, observational analysis, and metric findings into recommendations for senior leadership and Feeds and Search product strategy.
- Mentor data scientists in relevance evaluation, experimentation, causal reasoning, and statistical rigor.
- Share methodological advances internally and, where appropriate, externally.
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