Founded in 2017, Trust surface early-warning signals for churn. For example, develop and maintain LTV models that account for plan type, channel, upsell pathways, and membership behavior directly informing marketing budget allocation and partnership ROI decisions. Design and execute multi-touch attribution analysis across paid media, organic, AARP partnership, and advisor referral channels. Experimentation Lead the experimentation program end-to-end: hypothesis formation, experimental design, sample size and power analysis, execution in PostHog/Others, statistical analysis, and business recommendation. Build guardrails and standards for when and how to run experiments including when to apply experimental methods propensity score matching and when randomized testing isn't feasible. Partner with product and marketing teams to prioritize the test backlog based on expected impact and feasibility. Data Science help build team-wide analytical standards. Qualifications Core Experience 8-12+ years of experience in analytics, data science, or a closely related quantitative role. Prior experience in a high-growth startup environment you've operated with limited resources, ambiguous data, and shifting priorities and delivered anyway. Demonstrated data science background : you've built predictive models, designed and analyzed experiments, and applied statistical methods to business problems in production settings. Experience owning an analytics domain end-to-end not just answering requests, but generating the questions and driving decisions. Technical Skills SQL : Advanced window functions, CTEs, complex joins, query optimization on large datasets. Python : Proficient pandas, statsmodels, scikit-learn, matplotlib/seaborn; you write clean, reproducible analytical code. Experimentation : End-to-end A/B test ownership, power analysis, and working knowledge of experimental methods. Product Analytics : PostHog, Amplitude, Mixpanel, or equivalent funnel analysis, cohort analysis, event instrumentation. BI / Visualization :ThoughtSpot, Omni, or equivalent building dashboards that enable self-serve, not just answering one-off requests. Data Modeling : Familiarity with dbt and the analytical data modeling - like ActivitySchema, Star Schema etc… Bonus Points Experience in fintech, legaltech, insurtech, or another regulated consumer vertical . Familiarity with Bayesian methods or causal inference frameworks beyond standard A/B testing. Experience with product-led growth (PLG) motions or B2B2C business models. Background in subscription, membership, or low-frequency/high-intent consumer products . Contributions to building an analytics function from scratch metrics frameworks, data dictionaries, experimentation standards. Comfort working with an AI Data Analyst tool in a self-serve analytics context. Employee Benefits
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