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Open nowPosted 3 days ago

Senior Data Scientist I, Growth & Engagement

Fetch80 open roles

Pay
$159,945 – $188,171 a year
Where
US - Remote
Work mode
Remote
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Your applicationOpen nowSenior Data Scientist I, Growth & EngagementFetch · US - Remote

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Fetch's own form.

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. Fetch postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 3 days ago

Fetch median: 1 days open

The posting

About the Role:

As a Senior Data Scientist focused on growth and engagement, you'll help Fetch understand user behavior and use data to shape product decisions that improve retention and engagement. You'll partner with Product, Engineering, and Design to set clear KPIs, success criteria, and guardrails that your team actually uses to make decisions. You'll independently design credible experiments and deliver clear launch, iterate, or stop recommendations.

You'll also develop predictive models that serve as the intelligence layer for our lifecycle intervention systems, from anticipating user behavior to deeper insight into product experiments. These signals help the product team tailor interventions to the right users and drive user and revenue outcomes.

Beyond whether a change worked, you'll find out who benefits and whether gains persist past novelty, and use that to shape targeting and product priorities. This is an independent, hands-on role: you'll own questions from framing to recommendation, build reusable analyses, and become a trusted voice in your product area's decisions.

What you’ll do at Fetch:

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

Minimum Qualifications:

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.

Preferred Requirements:

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

This is a full-time role that can be held from one of our US offices or remotely in the United States.

Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. The base salary range for this position is $159,945 - $188,171. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.

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