Senior Data Scientist I, Growth & Engagement

Fetch; United States; Remote; CAD 120,000 - 170,000; Full time

Benefits offered by this job

AI tools in recruiting practices

Job summary

Fetch is seeking a Senior Data Scientist focused on growth and engagement to help understand user behavior and shape product decisions that improve retention and engagement.

You will develop predictive models and design experiments, partnering with Product, Engineering, and Design to set KPIs and guardrails, and own questions from framing to recommendation. This remote/Canada-friendly role emphasizes building reusable analyses and influencing product priorities.

Qualifications

• 5+ years in data science, ML, or applied analytics with ownership of complex problems in product settings.
• Experience owning end-to-end experiment design including power analysis and guardrails.
• Track record of using experiments to inform launches, iterations, or halts.
• Ability to frame causal questions and distinguish from prediction.
• Experience defining KPI frameworks for product workstreams.
• Proven impact on adoption, retention, engagement, revenue, or efficiency with ownership
• Experience identifying retention drivers and assessing persistence of gains.
• Experience applying predictive models to segment users or forecast outcomes.
• Strong SQL and at least one programming language (Python preferred).
• Experience with Snowflake, dbt, Airflow, Spark.

Responsibilities

• Independently design, execute, and interpret product experiments with power analysis and guardrails.
• Frame causal questions with populations, outcomes, and time horizons to show improvements.
• Define KPI frameworks linking product objectives to metrics and success criteria.
• Recommend launch/iterate/stop based on effect size, uncertainty, and business impact.
• Balance speed and rigor; identify when more evidence could change decisions.
• Convert ambiguous questions into structured, credible analyses and narratives.
• Link metric movements to behavioral drivers and rule out alternatives.
• Use analytics to map user journeys and inform product development, retention, and revenue.
• Evaluate differential effects across user segments without overinterpreting noise.
• Develop predictive models acting as the intelligence layer for lifecycle interventions.
• Tailor treatments using audience segments and signals to drive MAU and revenue.
• Produce reusable analyses and share knowledge across the team.

Skills

Data science leadership

Experiment design ownership

Causal framing

KPI frameworks

Influence launch decisions

Retention drivers

Predictive modeling / segmentation

SQL & Python proficiency

Large-scale data systems

Uncertainty communication

Education

Bachelor's degree in Statistics/Mathematics/CS/Engineering

Tools

Snowflake

dbt

Airflow

Spark

Job description

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 or any Canadian province (excluding Quebec), subject to appropriate work authorization. Canadian employment, if applicable, would be through Fetch's Canadian Employer of Record.

Benefits for Canada-based employees may differ slightly to comply with applicable law(s) (e.g., paid time off, Holidays, etc.).

Fetch may use technology tools that incorporate artificial intelligence ("AI") capabilities to support certain activities within our recruiting and hiring process:

• Reviewing and organizing applications and resumes to help our recruiting team manage high volumes of submissions, including automated review of fraud signals to identify bad actors;


• Identifying candidates whose stated qualifications and experience appear relevant to an open position;


• Scheduling and coordinating interviews with candidates;


• Generating interview summaries or notes to assist recruiters and hiring managers in their review; and


• Conducting research on open roles and candidate pools to improve the effectiveness of our recruiting practices.

Fetch does not use AI tools to make hiring decisions. All decisions regarding whether to advance a candidate, extend an offer, or decline an application are made by Fetch employees, who are predominantly members of our Talent Acquisition team and relevant Hiring Managers. AI tools serve only to assist the process of recruiting talent and to inform the human review of candidate information. In no way does Fetch allow AI systems to make, finalize, or override any employment decision.