hirly

Pedestal Health

Principal Data Scientist

Research Triangle Park, North Carolina

See how you match this job — and similar ones. Free.

Upload your resume and hirly scores it against this role at Pedestal Health first, then against similar open jobs, and shows where you fit and why.

PDF or DOCX, up to 12MB. No sign-up to see your matches.

Get past the screening software and onto a recruiter's desk

hirly rewrites your resume for this job — matching the keywords and skills in the posting, moving your most relevant experience to the top, and writing a cover letter to fit. About 30 seconds.

  • Keywords matched to this posting
  • Fit score before you apply
  • Cover letter included

Matched against 2.6M live jobs from 190,000+ employers in 200+ countries.

Tailor my resume for this job →

Apply from your AI assistant

Connect hirly to Claude and ask it to apply to this job. hirly tailors your resume, fills the employer’s form and asks before sending. ChatGPT: manual setup today.

Some employer sites stop an application at a CAPTCHA or sign-in and hand it back with a link. Applying needs a paid plan. Works with any assistant that supports MCP.

hirly's read of this role

Role family
Data & ML
Seniority
Lead / management
Country
US
Work mode
On-site / unstated
First seen by hirly
3 Oct 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

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.

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.

What You'll Do

Build and Scale Internal Tooling

You will 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

You will 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

You will 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

What You'll Bring

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

What we offer you

Hybrid work — 3 days/week in office to collaborate with the team

Comprehensive health, dental, and vision for you and your family

401(k) with company match

Generous PTO and company holidays

Paid parental leave

Hybrid role: Located in Research Triangle Park, North Carolina

If you are ready to be part of a team where your work truly matters—where your expertise is valued, your growth is supported, and your contributions help shape the future of healthcare—Pedestal Health is the place for you. We’re building something meaningful together, and we’d love for you to be a part of it.

Pedestal Health is an equal opportunity employer and seeks candidates from diverse backgrounds and abilities.

Original posting on Pedestal Health's site ↗

Listed on hirly, a job board. hirly is not the employer: Pedestal Health is hiring for this role.

Browse similar roles

Want this one?

Upload your resume and hirly rewrites it for this job and writes the cover letter — in about thirty seconds, before you sign up.

Tailor my resume for this job