Staff Data Scientist, Fetch

• 8+ years of experience in data science, analytics, quantitative strategy, or a related field, including ownership of complex product or business domains
• Advanced proficiency in Python and SQL, with experience building reusable analytical and modeling workflows
• Experience developing, evaluating, and operationalizing machine learning and predictive models, including feature engineering and translating model outputs into business decisions
• Strong foundation in statistics, experimental design, power analysis, metric selection, segmentation, and causal inference
• Experience applying causal inference techniques such as difference-in-differences, matching, synthetic controls, or related methodologies
• Experience developing models related to personalization, propensity, recommendations, forecasting, retention, or lifetime value
• Demonstrated ability to independently structure and solve ambiguous, high-impact problems while balancing analytical rigor with business urgency
• A track record of using analytics to drive measurable improvements in areas such as revenue, engagement, retention, product performance, or operational efficiency
• Proven ability to influence senior stakeholders across Product, Engineering, Marketing, and business teams without direct authority
• Experience mentoring analysts or data scientists and improving the quality of work across a broader team
• Exceptional communication and data storytelling skills, including the ability to explain sophisticated analytical concepts to non-technical audiences

Desirable

• Fetch is looking for a Staff Data Scientist to serve as the senior analytical and scientific leader for our Play business and broader Rewarded Apps portfolio
• Reporting to the Senior Director of User Analytics & Insights, you’ll partner with General Managers and leaders across Product, Marketing, Engineering, and other functions to shape how data, experimentation, and advanced analytics inform the future of Play
• This is a strategic, hands-on individual contributor role
• You’ll set the analytical direction for Play, identify the highest-value questions for the business, and lead complex analyses and modeling initiatives that influence product strategy, monetization, engagement, and long-term growth
• While this role has no direct reports, you’ll mentor analysts and data scientists, establish best practices, and raise the bar for analytical rigor across the organization
• Set the analytical and scientific strategy for Play, identifying high-impact opportunities across revenue, margin, engagement, retention, user experience, and advertiser performance
• Serve as a senior thought partner to business, Product, Marketing, and Engineering leaders, proactively defining the questions analytics should answer
• Translate ambiguous business and product challenges into structured analytical approaches that clearly communicate trade-offs, uncertainty, and expected impact
• Develop a deep understanding of the Play P&L and connect user behavior and product performance to revenue, margin, and marketplace outcomes
• Lead advanced analytics and data science initiatives across engagement, retention, monetization, personalization, segmentation, and marketplace dynamics
• Build predictive and machine learning models that identify behavioral patterns, forecast outcomes, prioritize opportunities, and inform product and business strategy
• Shape Play’s experimentation and measurement strategy, applying A/B testing, causal inference, quasi-experimental methods, and observational analysis as appropriate
• Develop scalable analytical assets, feature pipelines, modeling frameworks, and trusted measurement systems using Python and SQL
• Partner with Product and Engineering to operationalize models, scoring frameworks, and analytical outputs that improve the user experience and business performance
• Translate complex findings into clear recommendations for senior and executive audiences, framed around customer outcomes, financial impact, strategic risk, and opportunity cost
• Mentor analysts and data scientists, review high-impact analyses, and establish best practices for modeling, experimentation, documentation, reusable code, and data storytelling