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Salary: USD 126,900 - 151,800 / annual
BILL is a fintech company automating financial processes for businesses of all sizes. You will join the Product Analytics team as the analytics owner for the Accounts Payable (AP) product line, reporting to AP leadership with direct collaboration with Product and Engineering.
In this role, you own the analytics agenda for AP end-to-end. Rather than executing analyses that Product requests, you proactively identify product opportunities, data gaps, and automation candidates—then bring recommendations to Product that shape the roadmap. You will have real ownership and executive visibility from day one.
Key responsibilities include:
- Setting the analytics agenda for AP by finding opportunities and data gaps, bringing recommendations to Product
- Owning the AP analytics relationship: defining metrics and OKRs, running standing review cadences with Product, and driving deep-dive analysis practices
- Running experiments and applying causal inference to ensure recommendations are based on what actually moved a metric, not correlation
- Building and maintaining production-grade data models in dbt with tests, documentation, and version-controlled workflows
- Leveraging AI-assisted workflows by default and automating recurring reporting to free time for higher-value work
- Driving executive reporting and adoption across leadership and Product teams
- Presenting with confidence to senior stakeholders and defending metrics when contested
- Partnering with data engineering to validate data quality, metric definitions, and fixing documentation-to-logic drift
The role is hybrid with offices in San Jose, CA and Draper, UT. BILL is remote-first with in-person collaboration when it counts.
Requirements:
- 5+ years in product analytics, data science, or related quantitative field, with end-to-end domain ownership
- Track record of self-directed work: finding high-impact problems and shipping solutions
- Advanced SQL and strong Python
- Production experience with dbt or similar (models, tests, documentation, version-controlled review)
- Fluency in experimentation and causal inference with judgment on when experiments are the right tool
- Genuine AI-native fluency: you build with agentic coding tools already and know from experience where their output can and cannot be trusted
- Judgment to turn ambiguous questions into metrics frameworks and spine to defend them
- Data storytelling that turns complex findings into decisions product teams act on
- Must have authorization to work in the United States without visa sponsorship
