Senior Data Scientist

Fetch•Remote,

7d•$159,945 - $188,171

About The Position

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. We are seeking a Senior Data Scientist to serve as a key analytical partner within Fetch, owning complex analyses and measurement frameworks that inform product and business decisions. You will take primary analytical ownership of a product or business area, defining and monitoring core KPIs, identifying opportunities, and using experimentation, statistical modeling, and data-driven recommendations to improve user and business outcomes. You will partner cross-functionally with Product, Engineering, Marketing, and Data Product teams to turn ambiguous business questions into structured analytical approaches and actionable recommendations. Success in this role requires strong technical judgment, the ability to balance analytical rigor with business urgency, and the ability to connect data and model outputs to the underlying drivers of revenue, cost, and user behavior. Over time, you will take on increasingly sophisticated modeling and business case development while helping strengthen data literacy and analytical rigor across your team.

Requirements

5+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments.
Strong expertise in statistical modeling, experimental design, and causal inference.
Experience owning KPIs, measurement, or analytics for a product or business area and translating findings into actionable recommendations.
Demonstrated ability to structure ambiguous business problems and connect analytical findings to business drivers such as revenue, cost, conversion, retention, or user behavior.
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.
Proven ability to communicate complex technical insights, trade-offs, and confidence levels to technical and non-technical stakeholders.
Ability to work independently while navigating cross-functional dependencies and escalating broader trade-offs appropriately.
Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

Nice To Haves

Advanced degree in a quantitative discipline.
Experience deploying or operationalizing predictive or statistical models.
Background in consumer products, with experience using data to understand user behavior and inform engagement, retention, monetization, or other key customer outcomes.
Experience building frameworks that improve experimentation velocity and decision quality.
Familiarity with privacy-preserving data modeling and compliance standards such as GDPR or CCPA.
Experience mentoring junior data scientists, analysts, or interns.

Responsibilities

Independently design and execute complex analyses, statistical models, and measurement frameworks that inform product and business decisions.
Apply statistical methods such as experimental design, causal inference, predictive modeling, and Bayesian approaches based on the needs of the problem.
Translate ambiguous business questions into structured analytical approaches, identifying the appropriate metrics, methodologies, and data required.
Make thoughtful trade-offs between rigor, speed, precision, and practicality based on the business decision at hand.
Act as the primary analytical owner for a product or business area, defining and maintaining its core KPIs and measurement frameworks.
Proactively identify opportunities where analytics and experimentation can improve user behavior, revenue, conversion, retention, cost efficiency, or other key outcomes.
Design and analyze experiments, partnering with Product and Engineering to influence experimentation strategy and decision-making.
Connect metric movements and analytical findings to underlying business drivers, clearly articulating implications and recommended actions.
Quantify the impact of product and business initiatives and use those insights to influence roadmap and prioritization decisions.
Partner closely with Product, Engineering, Marketing, and Data Product stakeholders to inform team-level product and business decisions.
Communicate complex analyses through clear narratives and visualizations, including assumptions, trade-offs, confidence levels, and expected business impact.
Translate technical and analytical concepts for non-technical partners and navigate cross-functional dependencies effectively.
Increase data literacy by making metrics, analyses, and recommendations accessible and actionable for stakeholders.
Informally mentor junior data scientists and analysts, helping strengthen their technical judgment and analytical approaches.
Leverage tools and technologies such as Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to conduct and scale analytical work.
Apply strong practices in experimentation, model validation, reproducibility, and governance.
Use AI/ML tools thoughtfully to improve analytical workflows, automation, documentation, and anomaly detection while maintaining appropriate validation.