Senior Data Scientist, Fetch

• 5+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
• Experience owning experiment design end to end, including power analysis, primary metrics, and guardrails
• Track record of using experiment results to recommend launching, iterating, or stopping
• Ability to frame causal questions and distinguish prediction from causal impact
• Experience defining KPI frameworks for a product workstream
• Track record of influencing launch or roadmap decisions, with quantified impact on adoption, retention, engagement, revenue, or efficiency and clear personal ownership
• Experience identifying retention drivers and evaluating which users benefited and whether gains persisted
• Experience applying predictive models to segment users or forecast outcomes
• Strong proficiency in SQL and at least one programming language, preferably Python
• Experience working with large-scale data systems such as Snowflake, dbt, Airflow, or Spark
• Ability to communicate assumptions and uncertainty clearly
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field

Desirable

• Independently design, execute, and interpret product experiments for feature development, owning power analysis, primary metrics, and guardrails
• Frame causal questions by defining the population, outcomes, and time horizon needed to show whether a product change caused improvement
• Define KPI frameworks for teams to optimize around, linking product objectives to primary, secondary, and guardrail metrics with clear definitions and success criteria
• Recommend launch, iterate, or stop based on effect size, uncertainty, user value, and business impact
• Balance speed and rigor, and identify when more evidence could change a decision
• Turn ambiguous product questions into structured, credible analysis
• Connect metric movements to behavioral drivers and check alternative explanations
• Use product analytics to understand the user journeys and behaviors most impactful on retention and downstream outcomes, and use those insights to inform product development
• Evaluate how effects differ across user segments without overinterpreting noisy subgroup results
• Separate novelty and short-term gains from persistent, long-term value
• Develop predictive models that act as the intelligence layer for our lifecycle intervention systems
• Use audience segments and predictive signals to help the product team tailor treatment delivery to drive monthly active users and revenue, and validate that targeted treatments actually improve outcomes
• Build clear narratives that translate findings into product action, influencing priorities and launch decisions as the trusted analytical owner for your workstream
• Produce reusable analyses and share analytical knowledge across the team
• Leverage tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work