Rad AI
Business Analytics Engineer, Customer Insights
United States - Remote · Mid · Full-time
Sponsorship not specifiedDetected 6 days ago
PythonSQLSnowflakedbtData ScienceData VisualizationLLMsAgentic AIStatisticsComplianceValuationProcurementCustomer SuccessHIPAAHL7/FHIRPatient CareRadiologyCommunicationCollaboration
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odds of building a lasting career here
28Risky
Cap-exempt (no lottery)0
Sponsors this role45
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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About the role
- This is not a back-office reporting job.
Responsibilities
- 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.
- 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.
- Build durable data products, not one-off answers. Use dbt/Snowflake-style modeling practices so a metric built for one customer's dashboard is reusable for the next, instead of re-derived from scratch each time.
- part data engineer, part analyst, part customer-facing partner.
- 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.
Requirements
- The ability to make a confidence interval or caveat meaningful to that audience is as important as computing it correctly.
- 2-5 years in an analytics engineering, data science, or business/BI analyst role, ideally with some customer- or stakeholder-facing exposure (not purely internal reporting).
- Experience with a modern governed BI/semantic layer tool (we use Omni
- Comfort with ambiguity.
- Demonstrated ability to run discovery with external stakeholders: asking good follow-up questions, pushing back on vague asks, and landing on a defensible metric definition.
Nice to have
- Exposure to multi-tenant data governance (row-level security, per-customer data isolation) in an embedded analytics context.
- This role is open to candidates located anywhere in the United States.
- 11 Paid Company Holidays
Compensation
- Annual company-wide offsite
Benefits
- At Rad AI, we're on a mission to transform healthcare with artificial intelligence.
- Our investors include Khosla Ventures, World Innovation Lab, Gradient Ventures, Cone Health Ventures, and others-all backing our mission to empower physicians with cutting-edge AI.
- If you're ready to shape the future of healthcare, we'd love to have you on our team!
- Apply real statistical rigor to healthcare reporting data.
- Healthcare, health IT, or other regulated-industry data experience is a strong plus (HIPAA awareness, understanding why "it depends on the payer/facility/specialty" is often the right answer).
- Familiarity with radiology-specific concepts (PACS, HL7/FHIR, turnaround time, RVU, template-driven reporting) is a bonus, not a requirement.
Company info
- ABOUT RAD AI At Rad AI, we're on a mission to transform healthcare with artificial intelligence.
- Founded by a radiologist, our AI-driven solutions are revolutionizing radiology-saving time, reducing burnout, and improving patient care.
- Gather and translate requirements with customers and internal partners.
- 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.
- Use AI as a core part of your workflow. Direct AI agents to describe, model, and document data relationships, and verify what they produce.
- Comfortable in dbt/Snowflake-style modeling environments: building something once and having it hold up for the next ten customers.
- 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.
- Most recently, Rad AI was named to CNBC's Disruptor 50 https://www.cnbc.com/2025/06/10/2025-cnbc-disruptor-50-see-the-full-list-of-companies.html list, highlighting the innovation and momentum behind our mission.
This listing is sourced directly from Rad AI's careers page and normalized into a canonical job model.