Senior Data Scientist II, Ranking & Relevance
Summary
Senior data scientist who owns the analytics and experimentation strategy for ranking and relevance at Fetch, a consumer rewards marketplace — designing marketplace measurement frameworks, improving ranking models with engineers, and advising senior leaders on launch decisions. Stack includes Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS.
- 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
- 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
- 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
