Senior Data Scientist

Overview

Fliff unpacks sports gaming into social, free‑to‑play games for all types of sports fans. We've built a social sports gaming experience that allows users to compete for leader board positioning, to achieve badges and build their status within the game.

We are pioneering play‑for‑fun sports gaming, with our flagship social sports book experience that includes sweepstakes promotions and loyalty rewards. We provide sports fans with fun, engaging, and free‑to‑play alternatives to real money gaming.

Fliff is redefining the sports gaming experience by blending the fun of social play with the thrill of real‑money competition. What began as a pioneering social sports book has evolved into a multi‑vertical platform that is the fastest‑growing brand in sports gaming. As we continue to expand, we’re building a world‑class ecosystem of sports gaming experiences that span social, sweepstakes, and real‑money formats, giving every type of fan a way to play, compete, and connect.

We're hiring a Senior Data Scientist to embed within the marketing and growth function. You'll be the analytical backbone of how we acquire, retain, and grow our user base — building the models, dashboards, and analyses that drive smarter spend, sharper targeting, and a deeper understanding of our players. A core part of this role will be measuring the true impact of our marketing and promotional investments: not just short‑term lifts, but the long‑term effect on player behavior, retention, and lifetime value.

This role sits at the intersection of data engineering, modeling, and storytelling. You'll work directly with marketing, product, and finance leaders to turn raw behavioral and campaign data into decisions that move the business — and you'll own a problem end‑to‑end, from pulling and cleaning the data, to building the model, to presenting the insight to stakeholders.

• 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, Tik Tok, 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, Causal Impact, 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, Causal Impact, uplift modeling, propensity scoring) and when to apply each
• Solid grounding in marketing measurement concepts: attribution (MTA, MMM basics), incrementality, holdouts,…