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

Competitive compensation package, including base salary, benefits, and equity.
Unlimited/Flexible paid time off.
Health benefits, including medical, dental, and vision coverage, as well as generous parental leave.
Employee-sponsored 401(k).
$500 work-from-home stipend + Equipment & Accessories.
Opportunity for professional development in a dynamic, global setting.
A supportive, collaborative, and knowledge-driven workplace.

Hybrid

Structure: multinational company with our headquarters based in Philadelphia, and offices in New York, Austin, and Sofia, Bulgaria.

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Fliff is hiring a Senior Data Scientist