Pay
$118K–$193K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Senior Data Scientist I, Growth & Engagement
US - Remote
Live, first seen by us 14 days ago- Pay
- Pay not listedSimilar roles state $118K–$193K
- Work setting
Remote — work setting source
- Employment
Employment terms need review — employment source
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. This is a full-time role that can be held from one of our US offices or remotely in the United States. Role Responsibilities:
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingAs 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 partner with Product, Engineering, and Design to set clear KPIs, success criteria, and guardrails that your team actually uses to make decisions.
From the employer’s posting
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.
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.
What you’ll bring
All qualificationsCore experience
- 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
- Ability to frame causal questions and distinguish prediction from causal impact
- Experience defining KPI frameworks for a product workstream
- Experience identifying retention drivers and evaluating which users benefited and whether gains persisted
- Experience applying predictive models to segment users or inform lifecycle interventions
Preferred experience
- Experience evaluating incentives, lifecycle interventions, or personalization
- Experience validating whether targeted treatments improve outcomes
Qualification wording
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
Ability to frame causal questions and distinguish prediction from causal impact
Experience defining KPI frameworks for a product workstream
Experience identifying retention drivers and evaluating which users benefited and whether gains persisted
Experience applying predictive models to segment users or inform lifecycle interventions
Experience evaluating incentives, lifecycle interventions, or personalization
Experience validating whether targeted treatments improve outcomes
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 21, 202614 days ago
- Recorded sightings
- 28
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 14
Similar jobs usually come down between day 7 and day 55.
From 9,988 similar postings that came down. Coming down is not always a hire.
Fetch is hiring
29 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Scientist hiring is picking up
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- dbt
- Snowflake
- Spark
- Airflow
Where the posting names them
- 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 build reliable, reusable analyses and modeling workflows Minimum Requirements:
- Experience applying predictive models to segment users or inform lifecycle interventions - 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
- 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
Your next step
Complete your application on jobs.gem.com. The employer’s form will show what is required.
Already applied? Track this application
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
Meet Fetch AI & Data
AI & Data at Fetch sit at the center of how we understand our business, make decisions, and build intelligent products. The organization operates as an integrated AI & data ecosystem, spanning multiple disciplines, including data engineering, analytics engineering, machine learning, experimentation, and data platforms, all working together to turn data into durable business and customer impact.
Teams operate in complex problem spaces where requirements evolve, tradeoffs are constant, and the right answer is rarely obvious. Success depends on strong technical judgment, comfort with ambiguity, and the ability to gather context and make informed decisions while balancing quality, performance, scalability, and responsible use.
Practitioners across this org contribute hands-on to production systems, analytical foundations, and intelligent features. You will collaborate closely with product, platform, and engineering partners, help shape standards and best practices, and ensure our AI and data capabilities scale reliably as Fetch grows.
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.
This is a full-time role that can be held from one of our US offices or remotely in the United States.
Role Responsibilities:
- 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. Partner with Engineering to integrate predictive signals into treatment delivery, and use experiments to determine whether targeting improves retention, monthly active users, and revenue.
- 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 build reliable, reusable analyses and modeling workflows
Minimum Requirements:
- 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 inform lifecycle interventions
- 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
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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
US - Remote
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.