Staff Data Scientist, Rewarded Apps

Fetch•Remote,

2d•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

Experience in consumer technology, gaming, marketplaces, rewards, loyalty, or another high-frequency digital product.
Experience with modern analytics engineering tools and practices, including dbt, Git/GitHub, Spark, or similar technologies.
Experience working with businesses where user engagement, monetization, incentives, and marketplace economics are closely connected.
Experience combining advanced analytics with both product and commercial strategy.

Responsibilities

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.