# Senior Staff Data Scientist - Consumer Relevance **Company**: Reddit **Location**: Remote - Ontario, Canada **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/reddit/jobs/7974647?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_20ba1717-af4 ## Description We're looking for a Senior Staff Data Scientist to join our Consumer team. As a senior technical leader, 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. 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 Benefits: - 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 - Comprehensive Medical Benefits & Health Care Spending Account - Registered Retirement Savings Plan with matching contributions - Income Replacement Programs - Flexible Vacation & Paid Volunteer Time Off - Generous Paid Parental Leave ## Skills ### Required - Ph.D. in Statistics, Computer Science, Information Retrieval, Economics, or a related quantitative field - Metrics design and evaluation for ranking and recommendation systems - Causal inference and experimentation methodology - SQL and proficiency in R and/or Python for statistical computing - Experimental design, including power analysis, variance reduction techniques, and sequential testing ### Nice to have - 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 --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/reddit/jobs/7974647?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)