Staff Business Analyst

The Ethos Marketing team is a centralized, multi-disciplinary group of marketers, strategists, designers, storytellers, and makers who work across product groups and functions. We are designing the future and the today of the Ethos brand through every interaction within and outside of the product.

We are looking for a sharp, curious analyst who can go beyond dashboards and reporting to genuinely investigate how our offline and direct mail channels perform digging into attribution methodology, data layers, and vendor models rather than just consuming their output. This role sits at the intersection of business...

We need someone who can pick up real modeling and investigative work, not just BI support, to help the team keep pace with a growing offline and direct mail testing roadmap.

Investigate channel performance and business questions proactively, don't wait for a ticket to identify what's off, what's an opportunity, or what needs a deeper look
Go deep into attribution and underlying data layers to diagnose discrepancies, validate vendor claims, and evaluate new attribution or measurement vendors
Build models (e.g., response/lift models, allocation logic, simple regressions) to support offline and direct mail decision-making
Support the scaling of direct mail testing: manage multi-cell test mail merges and data pulls, dashboarding, and analysis to guide list selection and optimization
Develop easy-to-consume dashboards and frameworks to monitor and visualize KPIs
Communicate findings and insights to key stakeholders and influence channel managers to take action based on your recommendations

5+ years of experience in business intelligence, analytics, or data science at well-established firms or high-growth tech startups
3+ years of experience building and maintaining BI reporting in a tool like Hex or Mode on a regular basis
3+ years of experience working with SQL on a regular basis
Experience building statistical or predictive models outside of pure dashboard work
Experience working with Python for data analysis and model-building
Comfort working with ambiguous, incomplete, or conflicting data; attribution and vendor data are rarely clean
Strong written and verbal communication skills; able to translate technical findings into recommendations non-technical stakeholders can act on
A self-starter mentality, this role requires identifying problems and opportunities, not just executing on requests
Experience with experimentation and Bayesian measurement
Experience with direct mail, offline, or other non-digital marketing channels
Experience with dbt or maintaining data pipelines/marts
Familiarity with statistical techniques such as difference-in-difference, incrementality testing, and general linear models