Senior Data Science Manager, User Growth

The role

This senior management role sits inside Duolingo's Growth pillar and covers product data science, growth forecasting, and business intelligence. The leader reports to the head of data science and analytics and works closely with engineering, product, design, and finance. The position also includes building out a team that will expand over time.

What you would do

• Define the analytical direction and multi-quarter plan for growth data science, keeping it aligned with company and pillar objectives.
• Manage a group of 6–10 data scientists across experience levels, focused on their development and career progression.
• Foster a team environment that values rigor, inventive problem solving, and delivering measurable results.
• Recruit and bring on strong candidates as the group grows.
• Help refine the model of placing data scientists inside product squads while keeping the function unified.
• Lead the design, execution, and analysis of tests and studies affecting acquisition, onboarding, retention, and re-engagement.
• Raise and champion the standards used to draw trustworthy conclusions from experiments.
• Build and sustain forecasts for growth metrics in partnership with finance and product teams.
• Shape the growth organization's BI approach, covering shared metric definitions, self-service reporting, and tooling that boosts data literacy.
• Turn detailed findings into concise stories for executives and partner teams.

What they asked for

• Hands-on background applying data science or analytics to product and business questions.
• Prior experience leading a data science group of five or more people.
• Graduate training in data science, statistics, economics, or a similar quantitative discipline, or comparable proven analytical skill.
• Applied work with causal inference, experimentation, or machine learning.
• Comfort handling messy data where confounders and assumptions need careful judgment.
• Track record of building a strong team through hiring, growing people, and managing performance.
• Solid working knowledge of SQL, Python or R, and data tooling; able to go deep when needed, though not required to own production pipelines.
• Excellent written and spoken communication, turning complex analysis into clear recommendations.

Nice to have

• Background in a fast-scaling, data-heavy consumer technology business.
• Has shaped a long-term analytics direction that influenced product choices and business outcomes.
• Proven creation of forecasting or attribution systems at scale.
• Familiarity with the shift from a private growth company to a publicly traded one, including investor-facing reporting demands.
• Applied AI or machine learning to model user behavior, notification timing, or personalization.
• Engaged long-term learner of the product, shown by a notable active learning streak.