Growth Data Scientist

• Design LTV models that capture structural—not just seasonal—value
• Separate long-term signal from short-term promotions and seasonality
• Model retention and monetization dynamics that persist across cycles
• Stress-test models across cohorts launched in different macro and regulatory regimes
• Incorporate high-granularity features
• Build user- and cohort-level models that account for geography, regulatory or market-specific constraints, behavioral and product-usage signals
• Explicitly model heterogeneity rather than relying on global averages
• Ensure durability and generalization
• Validate models across multiple years, product launches, and seasonal cycles
• Monitor stability, drift, and cohort aging effects
• Revisit assumptions as the business and user base evolve
• Operationalize nuanced LTV
• Make LTV actionable at different resolutions (user, cohort, geo, channel)
• Partner with Growth to avoid overfitting CAC decisions to short-term spikes
• Align with Finance on long-range forecasting assumptions
• Be the voice of judgment
• Push back against simplistic or purely seasonal interpretations of value
• Clearly communicate uncertainty, confidence intervals, and limitations
• Prevent misuse of early-cohort or promotion-inflated signals

• 5–8+ years of experience as a Data Scientist in a consumer marketplace with meaningful seasonality
• Proven ownership of an LTV model that lived in production for multiple years, survived multiple seasonal cycles, and informed real budget, growth, or product decisions
• Deep experience with cohort-based modeling and survival analysis
• Deep experience with feature-rich segmentation (geo, behavior, product mix)
• Deep experience with de-biasing early lifecycle and promotion-heavy data
• Strong SQL and Python (or R)
• comfortable working with large-scale feature pipelines
• Demonstrated ability to explain why a model generalizes—not just that it performs

equity and benefits