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Duolingo is seeking a Senior Data Science Manager to lead the data science function within its User Growth pillar. With over 500 million users globally, Duolingo is expanding rapidly across new subjects and markets, creating significant demand for analytical leadership.
You will manage a team of 6–10 data scientists (spanning staff to junior levels), with responsibility for coaching, developing, and growing their careers while expanding the team through strategic hiring. This role owns all aspects of product data science, forecasting, and business intelligence for User Growth, reporting to the Head of Data Science & Analytics.
Key responsibilities include:
- Setting the analytical vision and roadmap for User Growth data science, aligned with company and pillar-level goals
- Leading a high-performing team while building a culture of professional growth, intellectual rigor, and impact
- Owning the design, execution, and interpretation of experiments and analyses that drive decisions on user acquisition, activation, retention, and reengagement
- Developing and maintaining user growth forecasting systems in partnership with Finance and Product
- Driving business intelligence strategy, including metrics layers, self-serve dashboards, and analytical infrastructure
- Translating complex findings into clear narratives for senior leadership and cross-functional partners
- Collaborating across Product Management, Engineering, Design, Marketing, and Finance
Required qualifications:
- 5+ years experience managing data science teams
- Graduate degree in Data Science, Statistics, Economics, or related quantitative field (or equivalent demonstrated expertise)
- Applied industry experience with causal inference, experimentation, and/or machine learning
- Proven track record building, leading, and growing high-performing data science teams
- Strong command of SQL, Python or R, and data/analytics engineering ecosystems
- Exceptional written and verbal communication skills
Preferred qualifications include experience at high-growth, data-intensive consumer technology companies; developing long-range analytical visions that shaped product strategy; and building forecasting systems or attribution models at scale.
