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Senior Data Scientist II - Relevance & Ranking
Canada - Remote
Live, first seen by us 17 days ago- Pay
- Pay not listed
- Work setting
Remote — work setting source
Listed location: Canada - Remote
Read the full posting- Employment
Employment terms need review — employment source
You'll be the core data science partner across the go-to-market, offer development, and offer delivery teams, translating commercial goals into measurable objectives, product requirements, and ranking improvements. You'll shape senior stakeholder decisions on launch, iteration, or stopping, and mentor others while establishing reusable methods that improve the team's scientific quality and execution. 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 own the analytical and experimentation roadmap for ranking and relevance at Fetch, partnering with Product, Engineering, and ML to improve advertiser outcomes, user value, and Fetch's long-term economics.
As the team's dedicated data scientist, you'll own the data science strategy for relevance and ranking, working closely with engineers on models that drive user engagement, rewards earned, and partner economic outcomes.
Own experiment design and turn results into launch recommendations
Design marketplace measurement that handles bias and advertiser interference
Build marketplace frameworks balancing advertiser, user, and platform value
Separate true incremental gains from value shifted between advertisers or time periods
Serve as core data science partner to commercial and offer teams
From the employer’s posting
You'll own the analytical and experimentation roadmap for ranking and relevance at Fetch, partnering with Product, Engineering, and ML to improve advertiser outcomes, user value, and Fetch's long-term economics.
As the team's dedicated data scientist, you'll own the data science strategy for relevance and ranking, working closely with engineers on models that drive user engagement, rewards earned, and partner economic outcomes. You'll build frameworks that balance advertiser incrementality and delivery, user relevance and retention, and platform revenue, reward costs, and margin.
Own experiment design and turn results into clear launch recommendations
Design measurement that holds up in a marketplace, where bias and interference between advertisers can distort results
Build marketplace frameworks that balance advertiser, user, and platform value with clear success criteria and guardrails for the trade-offs
Separate true incremental gains from value that's simply shifted between advertisers, surfaces, or time periods
Serve as the core data science partner to commercial and offer teams, turning business goals into measurable objectives and ranking improvements
What you’ll bring
All qualificationsCore experience
- 8+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
- Experience building reusable analytical assets and mentoring other analysts or scientists
- Strong proficiency in SQL and at least one programming language, preferably Python
- Experience owning the analytical roadmap and experiments for ranking, relevance, or recommendations
- Experience working with large-scale data systems such as Snowflake, dbt, Airflow, or Spark
- Ability to explain which questions you prioritized, which work you declined, and why
Preferred experience
- Experience in ad ranking or product search and ranking
Qualification wording
8+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
Experience building reusable analytical assets and mentoring other analysts or scientists
Strong proficiency in SQL and at least one programming language, preferably Python
Experience owning the analytical roadmap and experiments for ranking, relevance, or recommendations
Experience working with large-scale data systems such as Snowflake, dbt, Airflow, or Spark
Ability to explain which questions you prioritized, which work you declined, and why
Experience in ad ranking or product search and ranking
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
- 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
- Mentor others and build reusable methods that raise the team's scientific quality and execution - Leverage tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work Minimum Requirements:
- Strong modeling expertise - 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 Preferred Requirements:
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 own the analytical and experimentation roadmap for ranking and relevance at Fetch, partnering with Product, Engineering, and ML to improve advertiser outcomes, user value, and Fetch's long-term economics.
As the team's dedicated data scientist, you'll own the data science strategy for relevance and ranking, working closely with engineers on models that drive user engagement, rewards earned, and partner economic outcomes. You'll build frameworks that balance advertiser incrementality and delivery, user relevance and retention, and platform revenue, reward costs, and margin.
You'll be the core data science partner across the go-to-market, offer development, and offer delivery teams, translating commercial goals into measurable objectives, product requirements, and ranking improvements. You'll shape senior stakeholder decisions on launch, iteration, or stopping, and mentor others while establishing reusable methods that improve the team's scientific quality and execution.
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:
- Own the data science strategy and roadmap for ranking and relevance as the team's dedicated data scientist
- Partner closely with engineers to improve ranking models end to end, from defining what the model should optimize through experimentation
- Own experiment design and turn results into clear launch recommendations
- Design measurement that holds up in a marketplace, where bias and interference between advertisers can distort results
- Apply rigorous observational methods when experiments aren't possible, and defend the assumptions behind them
- Build marketplace frameworks that balance advertiser, user, and platform value with clear success criteria and guardrails for the trade-offs
- Separate true incremental gains from value that's simply shifted between advertisers, surfaces, or time periods
- Measure how changes impact different user and advertiser segments, distinguishing durable value from short-term lift
- Use findings to shape targeting and investment strategy, accounting for uncertainty
- Quantify commercial impact to guide senior leaders on priorities and product decisions
- Serve as the core data science partner to commercial and offer teams, turning business goals into measurable objectives and ranking improvements
- Communicate with technical and non-technical partners alike
- Mentor others and build reusable methods that raise the team's scientific quality and execution
- Leverage tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work
Minimum Requirements:
- 8+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments
- Experience owning the analytical roadmap and experiments for ranking, relevance, or recommendations
- Ability to explain which questions you prioritized, which work you declined, and why
- Cross-functional collaboration experience where product, sales, and engineering are all factors in your daily work
- Experience defining a marketplace KPI framework and resolving advertiser–user–platform trade-offs
- Track record of looking beyond a single metric's improvement to its effect on overall marketplace economics
- Experience designing measurement that accounts for selection bias, shared budgets, or spillovers
- Ability to defend experiment design and its assumptions, addressing selection bias, interference, or observational identification
- Demonstrated incremental, persistent business impact
- Track record of influencing strategy or launch decisions
- Examples of redirecting, pausing, or stopping initiatives based on quantified trade-offs
- Experience building reusable analytical assets and mentoring other analysts or scientists
- Strong modeling expertise
- 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
Preferred Requirements:
- Substantial experience with advertiser-funded consumer marketplaces, retail media, or ad platforms balancing advertiser, user, and platform incentives
- Experience in ad ranking or product search and ranking
- Consumer product analytics experience connecting behavior to retention and monetization
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 be the core data science partner across the go-to-market, offer development, and offer delivery teams, translating commercial goals into measurable objectives, product requirements, and ranking improvements. You'll shape senior stakeholder decisions on launch, iteration, or stopping, and mentor others while establishing reusable methods that improve the team's scientific quality and execution. 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.).