Bayesian Health
Software Engineer, Analytics
About this role
Early-stage healthcare AI startup seeks a Senior Software Engineer to design and build analytics infrastructure that enables clinical teams and product managers to monitor AI/ML product performance and investigate patient cases in real-time. You'll work across clinical, product, and engineering teams to provide visibility into product outcomes that drive expansion and revenue growth.
What you'll do
- Build monitoring infrastructure and automation to track product KPIs and success metrics across multiple clinical domains and customers
- Develop frameworks and tools for clinical teams to independently review and investigate patient cases meeting specific criteria
- Propose and implement foundational improvements to the data platform to support scalability as products and customer base expand
- Drive cross-functional alignment on product analytics with client success, clinical, product, data science, and engineering teams
- Translate requirements from non-technical stakeholders into robust technical solutions
What they're looking for
- Python and SQL
- AWS cloud platform experience
- Cloud-based data warehouse design and optimization
- Data transformation frameworks (e.g., dbt)
- Workflow orchestration tools
- BI tools (Tableau or Quicksight)
- Healthcare data handling (PHI/PII compliance)
- Cross-functional communication and collaboration
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Bayesian Health
Bayesian Health builds clinical AI/ML products that provide physicians and healthcare systems with real-time data insights and analytics to improve patient outcomes. The company is hiring infrastructure engineers, full-stack software engineers, forward deployed engineers, and senior engineers to develop scalable cloud platforms, EHR integrations, CI/CD pipelines, and analytics infrastructure that power enterprise healthcare deployments.
- Website
- bayesianhealth.com
Likely interview questions
- Walk us through a time you built scalable analytics infrastructure on AWS. What data warehouse technology did you use, and how did you handle performance optimization as data volume grew?
- Describe your experience with dbt or similar transformation frameworks. How have you used them to improve data quality and maintainability in production pipelines?