Senior Data Scientist I, Growth & Engagement

Fetch•US - Remote,

1d•$159,945 - $188,171

About The Position

As a Senior Data Scientist focused on growth and engagement, you will help Fetch understand user behavior and use data to shape product decisions that improve retention and engagement. You will partner with Product, Engineering, and Design to establish clear KPIs, success criteria, and guardrails, and independently design credible experiments that inform launch, iteration, or stop recommendations. You will develop predictive models that power lifecycle intervention systems, anticipating user behavior and informing product experiments. You will investigate who benefits from product changes and whether gains persist beyond novelty, using those findings to shape targeting and product priorities. This is an independent, hands-on role in which you own questions from framing through recommendation, build reusable analyses, and serve as a trusted analytical voice in product decisions. The role can be based at one of Fetch’s U.S. offices or performed remotely in the United States. The base salary range is $159,945–$188,171. Fetch describes its compensation package as including base pay, equity, and benefits; further benefits information is referenced at fetch.com/careers.

Requirements

At least 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.

Nice To Haves

Product analytics experience in a consumer app or digital product, across activation, engagement, retention, or monetization.
Experience evaluating incentives, lifecycle interventions, or personalization.
Experience validating whether targeted treatments improve outcomes.
Track record of sharing reusable analyses and knowledge with your team.

Responsibilities

Independently design, execute, and interpret product experiments, including power analysis, primary metrics, and guardrails.
Frame causal questions by defining the population, outcomes, and time horizon needed to assess whether a product change caused improvement.
Define KPI frameworks that connect product objectives to primary, secondary, and guardrail metrics, with clear definitions and success criteria.
Recommend whether to launch, iterate, or stop based on effect size, uncertainty, user value, and business impact.
Balance speed and rigor, identifying when additional evidence could change a decision.
Turn ambiguous product questions into structured, credible analyses.
Connect metric movements to behavioral drivers and check alternative explanations.
Use product analytics to understand user journeys and behaviors that affect retention and downstream outcomes, and inform product development.
Evaluate differences in effects across user segments without overinterpreting noisy subgroup results.
Distinguish novelty and short-term gains from persistent, long-term value.
Develop predictive models for lifecycle intervention systems.
Use audience segments and predictive signals to help tailor treatments, drive monthly active users and revenue, and validate whether targeted treatments improve outcomes.
Translate findings into clear narratives that influence product priorities and launch decisions, serving as the trusted analytical owner for the workstream.
Produce reusable analyses and share analytical knowledge across the team.
Use tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work.