Pedestal Health generates complete, longitudinal datasets with advanced analytics and vigorous causal inference methodologies to uncover deep insights on unmet needs, disease progression, and the efficacy and safety of new treatments in the real world. Backed by North Carolina Biotechnology Center.
About the role
We are seeking a Principal Data Scientist to join Pedestal Health's Quantitative Sciences (QS) organization. In this role, you will build the infrastructure and methods that make AI-assisted analytics and real-world data quality work fast, scalable, and genuinely trustworthy.
What they're looking for
- Build, maintain, and improve AI-powered tooling that lets the team generate commercial cohort counts and conduct feasibility reliably and repeatably
- Extend the same approach to other recurring analytic work, including generating and reviewing analysis code, and supporting protocol and analysis plan development
- Gather requirements from the internal teams who depend on these tools, treat them as users, and iterate on real feedback rather than assumed needs
- Scale adoption across the team through documentation, training, onboarding, and hands-on enablement
- Help define the guardrails for AI use in client-facing work: what data may be used, how outputs are reviewed and by whom, and how provenance is recorded
- Rethink how our data quality checks are built and run, so that assessing a new source or a refreshed schema no longer means redoing the work each time
More about this role
We are seeking a Principal Data Scientist to join Pedestal Health's Quantitative Sciences (QS) organization. In this role, you will build the infrastructure and methods that make AI-assisted analytics and real-world data quality work fast, scalable, and genuinely trustworthy.
Your work will span two connected areas. The first is AI-enabled analytics: building and maintaining AI tooling and workflows that let our team identify cohorts, review analysis code, and carry out recurring analytic work faster and more consistently. The second is AI-enabled data quality: designing automated and agentic approaches that scale across schemas, sites, and data refreshes, and that surface issues before they reach an analysis or a client.
This is a role about building capability, not about producing analyses. You will design, build, and scale the internal tools that change how our teams work with real-world data — and you will own them as products, with users, versions, quality standards, and a roadmap. This is a hands-on role that combines individual technical contribution with technical leadership, including mentorship and review of other data scientists' work. You will partner closely with...
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