SoftBank Vision Fund Staff Data Scientist, Rewarded Apps
Posted Updated
Summary
Senior hands-on data science leader for Fetch's Play business and Rewarded Apps portfolio: sets analytics strategy, builds predictive/ML models, causal inference, and experimentation frameworks in Python and SQL to guide product, monetization, and growth decisions, while mentoring analysts. Remote within the US or Canada (excluding Quebec).
About the Role:
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.
What You'll Do at Fetch:
- 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.
Minimum Qualifications:
- 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.
Preferred Qualifications:
- 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.
This is a full-time role that can be held from one of our US offices or remotely in the United States or any Canadian province (excluding Quebec), subject to appropriate work authorization. Canadian employment, if applicable, would be through Fetch’s Canadian Employer of Record.
Benefits for Canada-based employees may differ slightly to comply with applicable law(s) (e.g., paid time off, Holidays, etc.).
Fetch may use technology tools that incorporate artificial intelligence ("AI") capabilities to support certain activities within our recruiting and hiring process:
- Reviewing and organizing applications and resumes to help our recruiting team manage high volumes of submissions, including automated review of fraud signals to identify bad actors;
- Identifying candidates whose stated qualifications and experience appear relevant to an open position;
- Scheduling and coordinating interviews with candidates;
- Generating interview summaries or notes to assist recruiters and hiring managers in their review; and
- Conducting research on open roles and candidate pools to improve the effectiveness of our recruiting practices.
Fetch does not use AI tools to make hiring decisions. All decisions regarding whether to advance a candidate, extend an offer, or decline an application are made by Fetch employees, who are predominantly members of our Talent Acquisition team and relevant Hiring Managers. AI tools serve only to assist the process of recruiting talent and to inform the human review of candidate information. In no way does Fetch allow AI systems to make, finalize, or override any employment decision.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at .
