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Senior Data Scientist I - Core
Canada - Remote
Actively hiring, first seen by us 13 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
You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets. You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the 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.).
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting
What you’ll work on
Full postingYou'll help Fetch run on rigorous performance standards and scalable self-service analytics.
The work centers on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes like MAU, retention, ARPU, revenue, and margin.
You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets.
You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the team.
From the employer’s posting
You'll help Fetch run on rigorous performance standards and scalable self-service analytics. You'll help deliver our core company KPI framework, including its underlying model, while leading reusable, company-wide analytical frameworks and automation.
The work centers on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes like MAU, retention, ARPU, revenue, and margin. You'll validate forecasts and communicate sensitivity and uncertainty, so evidence turns into planning and operating decisions.
The work centers on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes like MAU, retention, ARPU, revenue, and margin. You'll validate forecasts and communicate sensitivity and uncertainty, so evidence turns into planning and operating decisions. You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets. You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the 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.
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
- Understanding of experimentation methodology sufficient to identify patterns, concerns, and frameworks that support product data scientists
- Experience working with multiple stakeholders to define KPIs and their hierarchy
- Ability to structure a complex space into a tackle-able project plan
- Experience building and validating models that connect user behavior and business drivers to retention, monetization, or company outcomes
- Ability to show sensitivity estimates, leading indicators, time horizons, and out-of-sample predictive performance
Preferred experience
- Experience owning KPI frameworks, causal measurement, or business planning
- Experience in centralized or platform data science
- Experience with experimentation platforms
- Experience with governed self-service analytics
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
Understanding of experimentation methodology sufficient to identify patterns, concerns, and frameworks that support product data scientists
Experience working with multiple stakeholders to define KPIs and their hierarchy
Ability to structure a complex space into a tackle-able project plan
Experience building and validating models that connect user behavior and business drivers to retention, monetization, or company outcomes
Ability to show sensitivity estimates, leading indicators, time horizons, and out-of-sample predictive performance
Experience owning KPI frameworks, causal measurement, or business planning
Experience in centralized or platform data science
Experience with experimentation platforms
Experience with governed self-service analytics
Posting record
Report an errorActively hiringFirst seen by us 13 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 21, 202613 days ago
- Recorded sightings
- 27
- 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
- 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
Where the posting names them
- Evaluate the accuracy, adoption, and sustained value of what you build - Build reproducible, tested analytical workflows in SQL and Python - Partner with Data Engineering on gold tables, data marts, and semantic layers
- Experience modeling multi-metric relationships - Strong skills in SQL, Python, statistics, causal inference, and forecasting - Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with evidence of accuracy, adoption, and decision impact
- Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with evidence of accuracy, adoption, and decision impact - Experience building robust SQL/Python analytical assets and partnering on productionization - Track record of documenting and sharing reusable methods
Your next step
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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:
You'll help Fetch run on rigorous performance standards and scalable self-service analytics. You'll help deliver our core company KPI framework, including its underlying model, while leading reusable, company-wide analytical frameworks and automation.
The work centers on trusted metrics: standardizing how impact is measured and connecting product and business drivers to outcomes like MAU, retention, ARPU, revenue, and margin. You'll validate forecasts and communicate sensitivity and uncertainty, so evidence turns into planning and operating decisions.
You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets. You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the 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.).
Role Responsibilities:
- Independently own a company wide KPI, modeling, or measurement workstream through delivery and adoption
- Contribute to our core company KPI framework, including its driver tree and sensitivity model
- Contribute analysis to planning and points allocation
- Define certified metrics and resolve conflicting definitions with partners
- Build KPI trees and decompositions that connect product and business drivers to MAU, retention, ARPU, revenue, and margin
- Model KPI drivers and quantify the direction, sensitivity, predictive strength, and time horizon of KPI-to-North-Star relationships
- Distinguish arithmetic identities, predictive associations, and causal effects
- Build scenarios and translate company goals into workstream targets
- Apply established observational causal methods with diagnostics and robustness checks, making identification assumptions explicit
- Build reproducible diagnostics and analytical tools grounded in certified data
- Evaluate the accuracy, adoption, and sustained value of what you build
- Build reproducible, tested analytical workflows in SQL and Python
- Partner with Data Engineering on gold tables, data marts, and semantic layers
- Partner with cross-functionally with teams such as Product and Engineering to drive adoption
- Make actionable recommendations and communicate sensitivity, assumptions, and uncertainty clearly
- Document and share reusable methods
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 working with multiple stakeholders to define KPIs and their hierarchy
- Ability to structure a complex space into a tackle-able project plan
- Track record of establishing trusted metric definitions and driving adoption across Product, Finance, Commercial, and Engineering
- Experience building and validating models that connect user behavior and business drivers to retention, monetization, or company outcomes
- Ability to show sensitivity estimates, leading indicators, time horizons, and out-of-sample predictive performance
- Experience using models in target setting or scenario planning while distinguishing prediction from causal impact
- Experience with ML modeling, including regression, causal inference methodology, and graph models
- Experience modeling multi-metric relationships
- Strong skills in SQL, Python, statistics, causal inference, and forecasting
- Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with evidence of accuracy, adoption, and decision impact
- Experience building robust SQL/Python analytical assets and partnering on productionization
- Track record of documenting and sharing reusable methods
- Understanding of experimentation methodology sufficient to identify patterns, concerns, and frameworks that support product data scientists
Preferred Requirements:
- Consumer product analytics experience connecting acquisition, activation, engagement, retention, and monetization
- Experience owning KPI frameworks, causal measurement, or business planning
- Experience in centralized or platform data science
- Experience with experimentation platforms
- Experience with governed self-service analytics
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
Canada - Remote
Working pattern and location restrictions need checking in the full posting.
- Work authorization
You’ll own defined KPIs, modeling, or measurement workstreams through delivery and adoption, make actionable recommendations, and translate company goals into workstream targets. You’ll work with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods with the 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.).