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Staff Data Scientist - Platform
Canada - Remote
Live, first seen by us 17 days ago- Pay
- Pay not listed
- Work setting
Remote — work setting source
Listed location: Canada - Remote
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Employment terms need review — employment source
Within your first year, you will define Fetch's MAU × ARPU measurement operating system, establish canonical metrics and semantic standards powering FetchGPT and executive reporting, and deliver strategic models such as marketing mix, elasticity, and incentive sensitivity that directly inform leadership decisions. 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.).
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingFetch is at a critical inflection point in how data and science inform the company's most important decisions.
We are seeking a Staff Data Scientist to serve as the company-wide scientific and measurement leader.
Define and own the company-level MAU × ARPU measurement framework
Set scientific rules and quality bar for experiments on pricing and incentives
Define canonical metric definitions across experimentation, dashboards and diagnostics
Own cross-company models such as marketing mix and elasticity for executive decisions
Design upskilling programs in statistics, causality, modeling and storytelling
From the employer’s posting
Fetch is at a critical inflection point in how data and science inform the company's most important decisions. With millions of monthly active users, rich item-level purchase data, and increasing investment in AI-driven products like FetchGPT, Fetch has an opportunity to establish a rigorous, scalable measurement and causal reasoning foundation that powers pricing, incentives, growth, marketing investment, and financial planning.
We are seeking a Staff Data Scientist to serve as the company-wide scientific and measurement leader. This role goes beyond traditional analytics or domain ownership. You will define how Fetch measures value, reasons about causality, and translates evidence into executive decisions. You will own core measurement frameworks, architect semantic and metric foundations, and set the scientific quality bar across analytics, experimentation, and strategic modeling.
Define and own Fetch's company-level measurement framework anchored in MAU × ARPU.
Set scientific rules for experimentation and validate high-risk tests such as pricing and incentives.
Define canonical metric definitions and unify logic across experimentation platforms, dashboards, and diagnostics.
Own cross-company models that drive executive decisions, including marketing mix modeling, elasticity and incentive sensitivity
Design upskilling programs in statistics, causality, modeling, and storytelling.
What you’ll bring
All qualificationsCore experience
- 8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company and/or org-level problems.
- Deep expertise in causal inference, experimental design, and observational analysis, with demonstrated ownership of high-stakes business decisions informed by causal evidence.
- Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models used by senior leadership.
- Proven ability to influence and support executive decision-making, including presenting trade-offs, uncertainty, and recommendations that directly impact strategy.
- Bachelor's degree in a quantitative field.
Preferred experience
- Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts used in executive planning.
- Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems with complex value trade-offs.
- Experience in ads ranking, relevance, retrieval, recommendation, or personalization systems.
- Experience in ad measurement, attribution, incrementality, experimentation, or optimization.
Qualification wording
8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company and/or org-level problems.
Deep expertise in causal inference, experimental design, and observational analysis, with demonstrated ownership of high-stakes business decisions informed by causal evidence.
Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models used by senior leadership.
Proven ability to influence and support executive decision-making, including presenting trade-offs, uncertainty, and recommendations that directly impact strategy.
Bachelor's degree in a quantitative field.
Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts used in executive planning.
Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems with complex value trade-offs.
Experience in ads ranking, relevance, retrieval, recommendation, or personalization systems.
Experience in ad measurement, attribution, incrementality, experimentation, or optimization.
Education & alternatives
- Exceptional written and verbal communication skills, with the ability to explain complex causal and modeling concepts to non-technical senior audiences. - Bachelor's degree in a quantitative field. Preferred Requirements:
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 18, 202617 days ago
- Recorded sightings
- 33
- 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
Fetch is hiring
29 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Scientist hiring is picking up
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Where the posting names them
- Champion best practices in experimentation design, model validation, and reproducibility. - Leverage modern analytics tooling such as Python, SQL, Snowflake, dbt, and experimentation platforms. Minimum Requirements:
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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:
Fetch is at a critical inflection point in how data and science inform the company's most important decisions. With millions of monthly active users, rich item-level purchase data, and increasing investment in AI-driven products like FetchGPT, Fetch has an opportunity to establish a rigorous, scalable measurement and causal reasoning foundation that powers pricing, incentives, growth, marketing investment, and financial planning.
We are seeking a Staff Data Scientist to serve as the company-wide scientific and measurement leader. This role goes beyond traditional analytics or domain ownership. You will define how Fetch measures value, reasons about causality, and translates evidence into executive decisions. You will own core measurement frameworks, architect semantic and metric foundations, and set the scientific quality bar across analytics, experimentation, and strategic modeling.
Within your first year, you will define Fetch's MAU × ARPU measurement operating system, establish canonical metrics and semantic standards powering FetchGPT and executive reporting, and deliver strategic models such as marketing mix, elasticity, and incentive sensitivity that directly inform leadership decisions.
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.).
Role Responsibilities:
Company Measurement and Causal Strategy
- Define and own Fetch's company-level measurement framework anchored in MAU × ARPU.
- Establish decision frameworks for pricing, incentives, and value trade-offs.
- Set standards for evidence quality, uncertainty, and confidence in decision-making.
- Define the causal reasoning model used across product, growth, marketing, and finance.
- Own the scientific capability roadmap including elasticity, value curves, MMM, and forecasting.
Semantic and Data Architecture
- Architect the semantic mart and metric logic powering FetchGPT and scalable insights.
- Define canonical metric definitions and unify logic across experimentation platforms, dashboards, and diagnostics.
- Partner with Analytics Engineering and Data Platform to build foundational data assets.
- Establish BI standards and eliminate redundant or conflicting dashboards.
Scientific Governance and Experimentation
- Serve as the quality bar for high-impact analytics and diagnostics.
- Review strategic analyses to ensure correct interpretation and mechanism alignment.
- Set scientific rules for experimentation and validate high-risk tests such as pricing and incentives.
- Ensure observational and experimental results reconcile cleanly.
- Create templates and interpretation guides to standardize rigor.
Strategic Modeling Ownership
- Own cross-company models that drive executive decisions, including marketing mix modeling, elasticity and incentive sensitivity, value expectation curves, strategic forecasting, and financial mechanism models supporting MAU × ARPU planning.
Org-Wide Scientific Leadership
- Raise the scientific maturity of the data science and analytics organization.
- Design upskilling programs in statistics, causality, modeling, and storytelling.
- Author best-practice modeling libraries and documentation.
- Serve as a technical anchor and thought partner for senior ICs across the org.
- Establish norms for rigorous, transparent, mechanism-driven insights.
Technical Excellence
- Apply advanced statistical and causal methods to company-level problems.
- Build scalable, production-ready analytical frameworks in partnership with engineering.
- Champion best practices in experimentation design, model validation, and reproducibility.
- Leverage modern analytics tooling such as Python, SQL, Snowflake, dbt, and experimentation platforms.
Minimum Requirements:
- 8+ years of experience in data science, economics, statistics, or applied research, including experience operating at Staff or Principal scope on company and/or org-level problems.
- Deep expertise in causal inference, experimental design, and observational analysis, with demonstrated ownership of high-stakes business decisions informed by causal evidence.
- Experience defining and owning company-level measurement frameworks, canonical metrics, or strategic models used by senior leadership.
- Proven ability to influence and support executive decision-making, including presenting trade-offs, uncertainty, and recommendations that directly impact strategy.
- Exceptional written and verbal communication skills, with the ability to explain complex causal and modeling concepts to non-technical senior audiences.
- Bachelor's degree in a quantitative field.
Preferred Requirements:
- Advanced degree in a quantitative discipline.
- Hands-on experience owning and maintaining strategic models such as marketing mix models, elasticity estimates, incentive sensitivity, or long-range forecasts used in executive planning.
- Experience in large-scale consumer products, marketplaces, ad-supported platforms, or incentive-driven systems with complex value trade-offs.
- Experience in ads ranking, relevance, retrieval, recommendation, or personalization systems.
- Experience in ad measurement, attribution, incrementality, experimentation, or optimization.
- Core ads product/platform experience - building or scaling systems that determine which ads are shown, how they perform, or how that performance is measured.
- Experience defining semantic layers, metric governance, or data contracts at scale across multiple teams or functions.
- Demonstrated track record of org-wide scientific leadership without direct people management, including setting standards, reviewing work, and raising the technical bar across teams.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Canada - Remote
Working pattern and location restrictions need checking in the full posting.
- Work authorization
Within your first year, you will define Fetch's MAU × ARPU measurement operating system, establish canonical metrics and semantic standards powering FetchGPT and executive reporting, and deliver strategic models such as marketing mix, elasticity, and incentive sensitivity that directly inform leadership decisions. 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.).