radai - Business Analytics Engineer, Customer Insights

• 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. • 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. • 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 • 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