Business Analytics Engineer, Customer Insights at Rad AI

Job description

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, and ranked by Deloitte 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 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) dire