Senior Data Scientist

About the Role

What You'll Do at Fetch

Analytics & Modeling

• 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.

Business Impact & Experimentation

• 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.

Collaboration & Influence

• 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.

Technical Excellence

• 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.

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.
• 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.

Preferred Requirements

• 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.