The Role
We're looking for a Data Scientist with strong analytical skills and a passion for solving complex systems problems to join our team. You will own the end-to-end growth funnel for our core participant pool, treating it as a dynamic growth engine. You'll work cross-functionally with product, engineering, and marketing teams, driving initiatives that attract, activate, and retain high-quality participants at scale. You'll have significant autonomy to design, build, and deploy models, develop measurement frameworks, and influence decisions that directly impact our platform's growth trajectory and business strategy.
What you’ll be doing
- Develop and own the quantitative framework that measures and optimizes the entire participant growth funnel (Acquisition, Activation, Retention, Referral), creating the core metrics and models that guide our growth strategy.
- Develop sophisticated models to understand user acquisition channels, predict participant lifetime value (LTV), and identify the key drivers of engagement and churn.
- Analyze and optimize the levers of growth, including referral programs, onboarding flows, and communication strategies to build a healthy, engaged participant base.
- Collaborate closely with product managers, engineers, and marketing partners to identify opportunities where data science can drive the strategy for participant acquisition and long-term retention.
- Synthesize complex analyses of our growth funnels and user journeys into actionable insights, presenting compelling data-driven narratives to influence strategic decisions.
- Design and analyze experiments to test hypotheses about user acquisition channels, onboarding experiences, and retention tactics.
- Partner with data engineers to enhance data pipelines and logging systems, creating a robust foundation for advanced growth modeling and user behavior analysis.
What you’ll bring
- Experience in modeling and analyzing user growth funnels, such as acquisition loops, user lifecycle marketing, or product-led growth dynamics.
- A strong background in building measurement systems and analytical frameworks, particularly using experimental design and advanced causal inference methods.
- Experience with or interest in working with human behavioral data, annotation/labeling systems, or projects involving human feedback for AI development and evaluation.
- Solid software engineering fundamentals with expertise in Python/R, SQL, AI/ML frameworks, and the modern data science stack.
- A toolkit spanning from classical statistical methods to state-of-the-art ML techniques (especially in predictive LTV, churn modeling, and marketing mix modeling), with knowledge of how to choose and apply the right tool for each unique problem.
- Proven ability to effectively communicate with and influence stakeholders across the organization, from engineers to executives.
- Ability to thrive in fast-paced environments and balance speed with quality.
- Strong prioritization skills, consistently focusing on high-impact work.
