Staff Data Scientist, Rewarded Apps

Senior analytical and scientific leader for Fetch’s Play and Rewarded Apps portfolio, guiding product strategy through advanced analytics, experimentation, causal inference, and predictive modeling. Requires 8+ years of experience, strong Python and SQL skills, and the ability to influence senior stakeholders and mentor data scientists.

About the job

Responsibilities

Set the analytical and scientific strategy for the Play business and Rewarded Apps portfolio across revenue, margin, engagement, retention, user experience, and advertiser performance.
Partner with Product, Marketing, Engineering, business leaders, and General Managers to define high-impact analytical questions and inform product and business strategy.
Translate ambiguous product and business challenges into rigorous analytical approaches, communicating 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 involving engagement, retention, monetization, personalization, segmentation, and marketplace dynamics.
Build predictive and machine learning models to identify behavioral patterns, forecast outcomes, prioritize opportunities, and support strategic decisions.
Shape experimentation and measurement strategy using A/B testing, causal inference, quasi-experimental methods, and observational analysis.
Develop scalable analytical assets, feature pipelines, modeling frameworks, and measurement systems using Python and SQL.
Partner with Product and Engineering to operationalize models, scoring frameworks, and analytical outputs.
Present recommendations to senior and executive audiences, emphasizing 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.

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 with causal inference techniques such as difference-in-differences, matching, synthetic controls, or related methods.
Experience developing models for personalization, propensity, recommendations, forecasting, retention, or lifetime value.
Ability to independently structure and solve ambiguous, high-impact problems while balancing analytical rigor with business urgency.
Track record of using analytics to improve revenue, engagement, retention, product performance, or operational efficiency.
Ability to influence senior stakeholders across Product, Engineering, Marketing, and business teams without direct authority.
Experience mentoring analysts or data scientists and improving work quality across a broader team.
Exceptional communication and data storytelling skills, including explaining 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 with businesses where user engagement, monetization, incentives, and marketplace economics are closely connected.
Experience combining advanced analytics with product and commercial strategy.

Compensation and Benefits

Competitive compensation package including base pay, equity, and benefits.
Equity for full-time employees.
Dollar-for-dollar 401(k) match up to 4%.
Medical, dental, and vision plans, including pet coverage.
Up to $10,000 per year in education reimbursement.
Flexible paid time off and 9 paid holidays, plus a year-end week-long break.
Paid parental leave and flexible return-to-work policies.

Skills

Python, SQL, Machine Learning, Predictive Modeling, Statistics, A/B Testing, Causal Inference, Feature Engineering, Personalization, Forecasting, dbt, Git, Spark, Data Storytelling