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Rad AI is building embedded analytics into its radiology reporting platform, giving healthcare systems and radiology groups self-service visibility into their own 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 will work directly with customer stakeholders (radiology administrators, quality leads, IT/informatics teams) to understand what they need to see, then build governed data models and dashboards that deliver accurate insights at scale across customers with very different data volumes, specialties, and reporting patterns. This is not a back-office reporting job; you'll translate ambiguous customer asks into rigorous, reusable metrics and explain findings when data looks unexpected.
Key responsibilities include:
- Connecting the new data platform to the existing warehouse, troubleshooting data quality issues per customer, and defining the data contract.
- Gathering and translating requirements through discovery conversations with radiology administrators, quality/ops leads, and informatics teams.
- Designing and building the semantic layer and dashboards with governed metrics, joins, and reusable models that work across customers rather than one-off reports.
- Applying statistical rigor to healthcare data: understanding when metrics need confidence intervals, risk-adjustment, or exclusion notes given small sample sizes, outliers, seasonality, and case-mix differences.
- Using AI agents as a core part of your workflow to describe, model, and document data relationships, with verification of outputs.
- Owning data quality end-to-end for customer-facing outputs: catching anomalies before customers do and documenting known caveats directly in deliverables.
- Building durable data products using dbt/Snowflake-style modeling so metrics are reusable across customers.
- Partnering cross-functionally with Product, Engineering, and Customer Success to prioritize high-impact metrics and dashboards.
- Presenting findings to radiologists, hospital administrators, and executives—making statistical concepts meaningful to clinical audiences without oversimplifying.
Radiology data is messy and high-stakes. You'll need strong judgment about when a metric is solid versus when it needs caveats, and the ability to explain those distinctions to non-data audiences.
REQUIREMENTS:
- 2–5 years in analytics engineering, data science, or business/BI analyst roles, ideally with customer- or stakeholder-facing exposure (not purely internal reporting).
- Demonstrated AI fluency with concrete examples from the last six months of how AI tools have meaningfully changed your data work, including use for describing, modeling, or documenting datasets and how you verify AI 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 judgment about when metrics need caveats.
- Experience with a modern governed BI/semantic layer tool (Omni, Looker, Sigma, ThoughtSpot, or similar) and a SQL/Python notebook or exploration environment (Hex, Mode, or similar).
- Comfort with dbt/Snowflake-style modeling environments: building something once so it holds 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 equivalent governed-BI modeling layer), not just consuming one.
- Comfort with ambiguity: energized rather than 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 defensible metric definitions.
- Excellent written and verbal communication; able to present data findings to non-technical, clinical audiences without dumbing down 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.
NICE TO HAVE:
- Experience building or maintaining customer-facing (vs. internal-only) analytics products.
- Exposure to multi-tenant data governance (row-level security, per-customer data isolation) in embedded analytics contexts.
- Prior experience at companies selling into hospitals/health systems, understanding procurement and IT review cycles.
