Business Analytics Engineer, Customer Insights

Rad AI is building out embedded analytics inside our reporting platform — giving radiology groups and health systems self-service visibility into their own reporting data: turnaround times, template usage, AI utilization, auto-launch accuracy, and more.

This role sits at the center of that effort: part data engineer, part analyst, part customer-facing partner. You’ll work with customers to understand what they actually need to see, then build the governed data models and dashboards that deliver it correctly, at scale, across customers with very different data volumes, specialties, and reporting patterns.

This is not a back-office reporting job. You’ll work with customer stakeholders (radiology administrators, quality leads, IT/informatics teams), with Product and an embedded analytics partner alongside you, translating ambiguous asks ("we want to see if our new templates are actually helping") into rigorous, reusable metrics and explaining them when a customer’s data looks stranger than expected.

- Get the data foundation right. Connect the new data platform to the existing warehouse, troubleshoot and fix data quality issues per customer, and help define the data contract that doesn’t exist yet.

- Gather and translate requirements with customers and internal partners. Join discovery conversations with radiology group administrators, quality/ops leads, and informatics teams to understand what they’re trying to measure and decide, not just what they initially ask for.

- Design and build the semantic layer and dashboards that serve customer requirements: governed metrics, joins, and reusable models that hold up across customers rather than one-off reports per account.

- Apply real statistical rigor to healthcare reporting data. Radiology data is messy and high-stakes: small sample sizes at some sites, outliers from edge-case studies, seasonality, case-mix differences across specialties and facility types. You'll need to know when a metric needs a confidence interval, a risk-adjustment, or an exclusion note, not just a number.

- Use AI as a core part of your workflow. Direct AI agents to describe, model, and document data relationships, and verify what they produce.

- Own data quality end-to-end for customer-facing outputs: catch and explain anomalies before a customer does, document known caveats (sample-size exclusions, data lag, definitional edge cases) directly in the deliverable.