Senior Product Data Analyst - AP & Network Analytics

BILL•San Jose, UT

6d•Remote

About The Position

As part of the Product Analytics team, you will be the analytics owner for our Accounts Payable product. This role is not about running analyses that Product hands you, but one that finds the analysis Product did not know to ask for: the metric that is quietly wrong, the automation nobody noticed, the deep dive that changes a roadmap call. You will work directly with Product, Engineering, and AP leadership, with real ownership and executive visibility from day one.

Requirements

5+ years in product analytics, data science or a related quantitative field, owning a domain end to end.
A track record of self-directed work: finding high-impact problems and shipping the fix.
Advanced SQL and strong Python.
Production experience with dbt or similar: models, tests, documentation, version-controlled review.
Fluency in experimentation and causal inference, and the judgment to know when an experiment is the right tool.
Genuine AI-native fluency. You build with agentic coding tools already, and you know from experience where their output can and cannot be trusted.
The judgment to turn an ambiguous question into a metrics framework, and the spine to defend it.
Data storytelling that turns a complex finding into a decision a product team acts on.

Responsibilities

Set the analytics agenda for AP.
Find product opportunities, data gaps, and automation candidates, and bring them to Product as recommendations that shape the roadmap.
Own the AP analytics relationship: metric and OKR definition, a standing review cadence with Product, and a deep-dive practice you drive.
Run experiments and apply causal inference, so recommendations rest on what moved a metric, not on correlation.
Build and maintain production-grade data models in dbt with tests, documentation, and version-controlled workflows.
Build with AI-assisted workflows by default, and automate recurring reporting to free your time for higher-value work.
Drive executive reporting and its adoption across leadership and Product.
Present with confidence to senior stakeholders, and hold your ground when a metric is contested.
Partner with data engineering to validate data quality and metric definitions, and fix documentation-to-logic drift when you find it.