Data Scientist, Apps

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.

• 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…