Senior Data Scientist

Build and maintain predictive models that drive marketing strategy, including player LTV, churn risk, CAC payback, and propensity-to-convert models

Own marketing attribution and incrementality analysis across paid channels (Meta, Google, TikTok, affiliates, influencers, etc.), helping the team understand what's actually driving growth

Quantify the causal impact and long-term business value of promotions, bonuses, and lifecycle campaigns, moving beyond surface-level engagement metrics to measure true incremental retention, monetization, and LTV impact

Apply causal inference techniques (geo experiments, synthetic control, diff-in-diff, CausalImpact, uplift modeling) to evaluate marketing investments where clean A/B tests aren't possible

Partner with growth marketers to design, run, and read out experiments: A/B tests, geo-tests, holdout studies, and creative tests

Develop and maintain dashboards and self-serve reporting that give marketing leaders real-time visibility into channel performance, cohort behavior, promo ROI, and funnel health

Clean, structure, and validate data across our marketing stack (ad platforms, MMP, internal event data, CRM) and partner with data engineering to improve our data models where needed

Translate complex analyses into clear, actionable recommendations for non-technical stakeholders

Continuously look for opportunities to automate, improve, and scale how the marketing team uses data

5+ years of experience as a data scientist, marketing analyst, or growth analyst, ideally in a consumer app, gaming, fintech, or subscription business

Strong SQL skills and comfort working with large, messy behavioral datasets

Hands-on experience building predictive models in Python (LTV, churn, propensity, segmentation, etc.) using libraries like scikit-learn, XGBoost, or similar

Experience evaluating the long-term and incremental impact of marketing and promotional spend: you understand the difference between who responded and who was actually influenced

Working knowledge of causal inference methods (diff-in-diff, synthetic control, CausalImpact, uplift modeling, propensity scoring) and when to apply each

Solid grounding in marketing measurement concepts: attribution (MTA, MMM basics), incrementality, holdouts, cohort analysis, and unit economics (CAC, LTV, payback)

Experience with experimentation: designing tests, sizing them, and reading them out with statistical rigor

Proficiency with at least one BI/visualization tool (Looker, Tableau, Mode, Sigma, etc.)

Strong communication skills: you can explain a model to a marketer and a campaign result to a CFO with equal clarity

A bias toward action: you'd rather ship a useful 80% answer this week than a perfect one next quarter

Experience with mobile measurement partners (AppsFlyer, Adjust, Singular) and/or paid media APIs

Familiarity with cloud data warehouses (Snowflake, BigQuery, Redshift) and dbt

Background in gaming, sports betting, fantasy sports, or another high-velocity consumer category

Experience modeling promo and bonus economics in a gaming, fintech, or e-commerce context