• Serve as the technical authority on experimentation methodology across Consumer, setting standards for design, analysis, and interpretation of experiments in a complex, networked environment • Tackle the hardest experimentation problems at Reddit, including spillover and network effects, interference between treatment and control, two-sided experimentation, and long-run effect estimation • Develop and advance methods for causal inference in settings where standard randomization assumptions are violated, such as cluster-randomized designs, switchback experiments, and synthetic control approaches • Design experimentation frameworks and guardrail metrics that account for ecosystem-level effects, ensuring product teams can measure true causal impact rather than biased local estimates • Identify opportunities where improved experimentation methodology can unlock product insights that were previously unmeasurable or ambiguous • Build and scale self-serve experimentation tools, platforms, and best-practice documentation that increase experimentation velocity and literacy across product, engineering, and design teams • Influence the long-term product strategy by driving learning through well-designed experiments and translating experimental results into clear, actionable recommendations for senior leadership • Mentor and elevate other data scientists across the organization on experimentation best practices, causal reasoning, and statistical rigor • Publish and share methodological advances internally and, where appropriate, externally to contribute to the broader experimentation and causal inference community

• 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

• 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

• Define and drive a clear analytics vision aligned to client engagement, media performance, and marketing measurement priorities. • Champion a data-driven culture by embedding analytics into strategic planning, campaign optimization, and decision-making processes. • Lead the development and execution of measurement frameworks tailored to each client’s objectives, ensuring alignment with business goals. • Translate data into actionable insights by identifying performance drivers, resolving anomalies, and crafting compelling narratives that demonstrate media impact. • Establish and maintain robust testing frameworks that are statistically valid and operationally scalable. • Drive confidence and alignment by delivering actionable insights rooted in analytics, business strategy, and persuasive communication. • Provide business feedback as a key stakeholder in the development of standardized dashboards and advanced analytics to measure the effectiveness of campaigns. • Leverage predictive and prescriptive analytics to identify trends, opportunities, and risks related to client behavior and retention. • Utilize advanced analytics to quantify marketing impact and ROI, and partner cross-functionally to diagnose and address performance irregularities. • Implement robust A/B and multivariate testing and experimentation protocols to optimize campaigns and maximize impact.