Senior Data Scientist, Referrals

<h2><span style="font-family: helvetica, arial, sans-serif;">About the role</span></h2> <p>We are hiring a Sr. Data Scientist, Referrals to help us understand how our referral investment drives member acquisition and long-term value. Unlike traditional paid channels, referrals operate through member-to-member advocacy, dual-sided incentives, and viral loops, requiring a distinct measurement lens that accounts for organic bleed, incentive sensitivity, and long-term referred member behavior.&nbsp;</p> <p>As our Sr. Data Scientist, Referrals, you will partner closely with the Referrals team to measure, optimize, and scale spend across the referral channel. You will bring expertise in campaign sizing, operational planning, and channel forecasting, helping the team plan confidently and allocate resources effectively. You will bring rigor to how we evaluate incrementality, attribution, and efficiency, moving us to a defensible view of what our referral dollars actually deliver.</p> <p><span style="font-family: helvetica, arial, sans-serif;">The base salary offered for this role and level of experience will begin at $133,000 and up to $185,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.</span></p> <h2><span style="font-family: helvetica, arial, sans-serif;">In this role, you can expect to</span></h2> <ul> <li>Partner with Referrals to define KPIs and measurement frameworks across campaigns and audiences</li> <li>Size and scope referral campaigns in partnership with marketing, product and finance: translating acquisition goals into expected spend, volume, and ROI projections ahead of launch, and supporting operational planning for campaign execution</li> <li>Build and maintain forecasting models for the referral channel, projecting member acquisition volume, cost, and downstream LTV under different spend scenarios, seasonality assumptions, and campaign configurations</li> <li>Develop attribution approaches that reconcile platform-reported performance with observed member behavior, accounting for the complexity of dual-sided referral incentives and organic word-of-mouth overlap.</li> <li>Design and analyze geo experiments, holdouts, and platform lift studies to quantify incremental lift and separate true impact from correlation</li> <li>Build and maintain dashboards that give clear visibility into spend, CAC, funnel conversion, and downstream LTV by campaign</li> <li>Collaborate with Data Engineering to improve tracking, data quality, and the pipelines behind spend, click, and conversion data</li> <li>Translate complex analyses into clear, actionable recommendations for cross-functional leadership</li> </ul> <h2><span style="font-family: helvetica, arial, sans-serif;">To thrive in this role, you have</span></h2> <ul> <li>4+ years of experience in referral programs and in marketing, growth, or product analytics. Experience in FinTech preferred.</li> <li>Experience with marketing campaign planning and sizing: comfortable working from business goals backward to estimate required spend, expected member volumes, and unit economics, with an understanding of how campaigns are operationally structured and executed</li> <li>Forecasting experience for a marketing or growth channel: building models that project forward-looking performance and communicating forecast uncertainty and scenario sensitivity to cross-functional stakeholders</li> <li>Strong experience designing and evaluating incrementality tests:&nbsp; geo experiments, holdouts, and platform lift studies</li> <li>Working knowledge of causal inference methods including experiment design, holdout analysis, and techniques for isolating organic from incentive driven behavior</li> <li>Advanced SQL skills and proficiency in Python or R for analysis and modeling</li> <li>Experience building dashboards (e.g., Looker, Tableau, or similar BI tools) that drive stakeholder decision-making</li> <li>A strong understanding of attribution concepts and the challenges of measurement in a privacy-constrained, cross-device environment</li> <li>The ability to communicate complex findings clearly to both technical and non-technical partners and influence decisions with data</li> </ul> <p><span style="font-family: helvetica, arial, sans-serif;">#LI-Hybrid #LI-AM1</span></p><div class="content-conclusion"><h2><span style="font-family: helvetica, arial, sans-serif;"><s