Principal, Quantitative Scientist
Pedestal Health
| Company | Pedestal Health |
| Category | Science & Research |
| Location | Research Triangle Park |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 9 Apr 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Pedestal Health (formerly Target RWE) combines deep health system partnerships, longitudinal data curation at scale, and modern study design to build continuous, high-quality evidence for life sciences organizations. The company partners with pharmaceutical and biotech companies, providers and payers to generate rigorous, multimodal evidence that supports clinical, regulatory and strategic decision-making across the full lifecycle of care. Pedestal Health is committed to advancing medical decision making to improve outcomes for patients. Pedestal Health is a Highlander Health company. Learn more at pedestalhealth.com .
Overview
We are seeking a Principal to join Pedestal Health’s Quantitative Sciences (QS) organization. In this role, you will help shape how we design, execute, and communicate real-world clinical studies using Pedestal's data.
This is a highly cross-functional and client-facing role. You will bring strong analytical and scientific expertise, paired with the ability to apply the right level of methodological rigor for the question at hand, balancing credibility, practicality, and clarity. You will work closely with internal teams and external partners to ensure analyses are scientifically sound, clearly communicated, and aligned with real-world use cases.
You will serve as a key representative of Pedestal’s scientific capabilities, engaging directly with pharmaceutical and biotech partners, supporting client discussions, and helping translate complex analytical approaches into clear, actionable insights.
What You’ll Do
Applied Methodology and Study Design
You will ensure that Pedestal’s analyses are scientifically rigorous, practical, and aligned with real-world use.
Guide the design of observational studies using real-world data, including retrospective and prospective analyses
Apply statistical and epidemiologic methods in a practical, fit-for-purpose way
Develop and review study protocols and statistical analysis plans
Ensure analyses are reproducible, interpretable, and aligned with regulatory and scientific expectations
Partner with internal teams to translate research questions into practical analytic approaches
Client-Facing Scientific Leadership
You will serve as a key scientific voice in client and partner interactions.
Participate in and lead client meetings to explain study design, methodology, and results
Translate complex analytical concepts into clear, accessible language for diverse audiences
Support business development efforts by helping communicate Pedestal’s scientific approach and data value
Represent Pedestal in external settings, including conferences, scientific collaborations, and partner discussions
Build trust with clients by providing thoughtful, pragmatic, and credible scientific guidance
Cross-Functional Collaboration
You will connect data, analytics, and business needs across the organization.
Partner closely with Product, Engineering, Medical Science, Clinical Operations, and Commercial teams
Help shape analytic strategies that align with both scientific goals and customer needs
Provide guidance to the Quantitative Sciences team on study design and interpretation
Mentor team members on balancing methodological rigor with practical execution
Contribute to internal best practices for scalable, high-quality evidence generation
What You’ll Bring
PhD or MS in biostatistics, epidemiology, health economics, data science, or a related field
10+ years of experience working with real-world data (EMR, claims, registry, or clinical trial data)
Strong foundation in observational study design and applied statistical methods; experience with real-world pragmatic clinical trial designs is a plus
Demonstrated ability to apply methods pragmatically, selecting the right approach for the problem
Willingness to engage directly with data and analyses as needed, with working knowledge of R and/or SQL to
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