Principal Data Scientist

Pedestal Health•Research Triangle Park, NC

19h•Hybrid

About The Position

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 Product, Engineering, Medical, and Commercial teams, and report to the Head of Quantitative Sciences.

Requirements

MS or PhD in data science, statistics, computer science, computational biology, or a related quantitative field; equivalent practical experience will be considered
Experience working with real-world healthcare data — EHR, claims, or registry — including direct familiarity with its structural and quality challenges
A track record of building internal tools, platforms, or analytic products used by other people, and owning them through multiple versions — not solely a record of delivering analyses
Strong programming ability in Python and SQL, with experience in a cloud data warehouse environment such as Snowflake
Demonstrated ownership of a data quality, testing, or observability system end to end, not only authoring individual checks
Practical experience building with large language models beyond prototyping, including prompt and workflow design and a clear point of view on how to evaluate whether an LLM-based system is actually working
Comfort working in raw, messy, poorly documented source data, and the curiosity and persistence to figure out what it actually contains
Experience working cross-functionally with product and engineering partners, and the ability to hold a technical line constructively when priorities compete
Clear written and verbal communication, including the ability to explain a method's limitations as readily as its results
Interest in growing into technical leadership, including mentoring and reviewing the work of other data scientists

Responsibilities

Build and Scale Internal Tooling: Make AI a dependable part of how our analytic work gets done, not an occasional shortcut.
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.
Own what keeps this tooling trustworthy over time (how it is tested against known-correct results, how updates are validated before release, and how performance is monitored as the underlying data evolves), and where human review remains mandatory.
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.
AI-Enabled Data Quality: Design the infrastructure that tells us whether our data is trustworthy, before anyone else has to find out.
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.
Move quality assessment beyond manual review and spreadsheet outputs, toward an automated approach with a durable record of what was checked, what was found, and how it was resolved.
Determine where AI and agentic approaches genuinely add leverage in this work, and where deterministic, reproducible checking should remain the foundation.
Define how we will know the system is working, including whether the people who receive quality signals continue to trust them and act on them.
Partner with Engineering on pipeline integration, orchestration, and monitoring.
Cross-Functional Collaboration and Team Development: Connect data science to the teams that build, sell, and deliver on top of it.
Partner closely with Product, Engineering, Medical Science, Clinical Operations, and Commercial teams.
Advocate effectively for the quality and validation work that AI-assisted products require, including in roadmap and prioritization discussions.
Keep pace with developments in AI tooling and methods, and actively push what proves useful into our standards, training, and tooling.
Provide technical oversight, code review, and mentorship to other data scientists, and own the standards their work is measured against.
Help grow the data science group as it expands.