Business Analytics Engineer, Customer Insights

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. With one of the largest proprietary radiology report datasets in the world, our AI has helped uncover hundreds of new cancer diagnoses and reduced error rates in tens of millions of radiology reports by nearly 50%. Rad AI has secured over $140M in funding, including a recently oversubscribed Series C ($68M round) led by Transformation Capital, bringing our valuation to $528M. 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. Our latest advancements in generative AI are used by thousands of radiologists daily, supporting more than one-third of radiology groups and healthcare systems and nearly 50% of all medical imaging in the U.S. at partners including Cone Health, Jefferson Einstein Health, Geisinger, Guthrie Healthcare System, and Henry Ford Health. Recognized as one of the most promising healthcare AI companies by CB Insights and AuntMinnie https://www.radai.com/news/auntminnie-recognizes-rad-ai-omni-reporting-as-2023s-best-new-radiology-software, and ranked by Deloitte https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/fast500-winners.html as the 19th fastest-growing company in North America, we are building AI-powered solutions that make a real impact. 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. If you’re ready to shape the future of healthcare, we’d love to have you on our team! ABOUT THE ROLE 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. WHAT YOU'LL DO - 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. - 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. - Partner cross-functionally with Product, Engineering, and Customer Success to prioritize which metrics and dashboards actually move the needle for customers versus which are just "nice to have." - Explain data to non-data audiences. You’ll present findings to radiologists, hospital administrators, and executives who are expert clinicians but not data scientists. The ability to make a confidence interval or caveat meaningful to that audience is as important as computing it correctly. WHAT YOU'LL BRING - 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). Hunger and curiosity matter more to us than years. - Demonstrated AI fluency. Concrete examples from the last six months of how AI tools have meaningfully changed the way you work with data, including how you’ve used them to describe, model, or document datasets and how you check their output. - Strong SQL — comfortable writing and optimizing complex queries, not just filtering existing tables. - Working statistics fluency: confidence intervals, significance testing, sample-size considerations, handling outliers and skewed distributions, and the judgment to know when a metric needs a caveat versus when it's solid. - Experience with a modern governed BI/semantic layer tool (we use Omni; Looker, Sigma, ThoughtSpot, or similar is great) and a SQL/Python notebook or exploration environment (we use Hex; Mode or similar is fine). - Comfortable in dbt/Snowflake-style modeling environments: building something once and having it hold up for the next ten customers. - Direct experience defining or maintaining a semantic layer (entity/metric definitions, joins, governed fields, e.g., LookML, dbt Semantic Layer, or an equivalent governed-BI modeling layer), not just consuming one someone else built. - Comfort with ambiguity. You’re energized, not rattled, by undefined problems, messy data, and no existing playbook. - 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. - Excellent written and verbal communication, able to present data findings to a non-technical, clinical audience without dumbing down the substance. - 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. We'll teach the domain if the analytical and statistical instincts are there. NICE TO HAVE - Experience building or maintaining a customer-facing (vs. internal-only) analytics product, i.e., you understand that a dashboard a paying customer sees is held to a different bar than an internal Slack chart. - Exposure to multi-tenant data governance (row-level security, per-customer data isolation) in an embedded analytics context. - Prior experience in a company selling into hospitals/health systems, understanding procurement and IT review cycles. Join our world-class team as we build and deploy AI solutions that empower physicians and transform patient care—making a meaningful impact on millions of lives. Driven by our mission,  we prioritize transparency, inclusion, and close collaboration, bringing together exceptional people to revolutionize healthcare. If you're passionate about driving innovation and delivering impactful healthcare solutions, we'd love to hear from you! To learn more about what it's like to work at Rad AI, visit https://www.radai.com/life-at-rad-ai and be sure to follow us on LinkedIn https://www.linkedin.com/company/radai/?utm_campaign=Recruiting_2026&utm_content=recruiting-job-posting-website&utm_source=recru%5B%E2%80%A6%5Db-posting-website to stay up to date! Location Details: For roles listed as San Francisco - Onsite: - This role will be based in our San Francisco office and we expect employees to work onsite four days per week. The remaining time may be worked remotely or onsite, depending on team and business needs. For roles listed as United States - Remote: - This role is open to candidates located anywhere in the United States. For roles listed as San Francisco - Onsite + United States - Remote: - We will prioritize candidates who can work onsite four days per week in San Francisco, while also considering remote candidates located anywhere in the United States. For US-Based Full-Time Roles, Rad AI offers a variety of benefits, including: - Comprehensive Medical, Dental, Vision & Life insurance - HSA (with employer match), FSA, & DCFSA  - 401(k) - 11 Paid Company Holidays - Flexible PTO policy - Annual company-wide offsite - Periodic team offsites - Annual equipment stipend - For roles based outside the US, your recruiter can share more details At Rad AI, we value diversity and provide equal employment opportunities (EEO) to all employees and applicants without regard to race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. Please be vigilant regarding job scams. We advise all candidates to apply directly through our official careers page. Our recruiters will use email addresses with the domain @radai.com http://radai.com or [email protected].