# Staff Data Scientist, Rewarded Apps

**Company:** [Fetch](https://hotfix.jobs/companies/fetch)
**Location:** Remote
**Role:** Data Science
**Experience:** 8+ years
**Skills:** Python, SQL, Machine Learning, Predictive Modeling, Statistics, A/B Testing, Causal Inference, Feature Engineering, Personalization, Forecasting, dbt, Git, Spark, Data Storytelling
**Posted:** 2026-09-09

> 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.

## Job Description

## 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.

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