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Salary: USD 127,000 - 223,000 / annual
Ethos is a life insurance technology company transforming how life insurance is bought, sold, and underwritten through a three-sided platform serving consumers, agents, and carriers. The company offers instant, accessible products with no medical exams required.
The Marketing team is a centralized, multi-disciplinary group designing the Ethos brand through every customer interaction. This Staff Business Analyst role sits at the intersection of business analytics and data science, focusing on offline and direct mail channel performance.
You will investigate channel performance proactively, going deep into attribution and data layers to diagnose discrepancies, validate vendor claims, and evaluate new measurement vendors. Key responsibilities include building statistical and predictive models (response/lift models, allocation logic, regressions) to support offline and direct mail decision-making; managing multi-cell test mail merges, data pulls, dashboarding, and analysis to guide list selection and optimization; developing easy-to-consume dashboards and KPI frameworks; and communicating findings to stakeholders to influence action.
This is not a BI support role—you'll own real modeling and investigative work to help the team keep pace with a growing offline testing roadmap. You'll identify problems and opportunities independently, stress-test assumptions, and surface answers before being asked.
Required: 5+ years in business intelligence, analytics, or data science at established firms or high-growth startups; 3+ years building and maintaining BI reporting in tools like Hex or Mode; 3+ years working with SQL regularly; experience building statistical or predictive models beyond dashboards; Python proficiency for data analysis and modeling; comfort with ambiguous, incomplete, or conflicting data; strong written and verbal communication; self-starter mentality.
Preferred: experimentation and Bayesian measurement experience; direct mail or offline marketing channel background; dbt or data pipeline/mart maintenance; statistical techniques such as difference-in-difference, incrementality testing, and general linear models.
