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Principal, Quantitative Scientist

Pedestal Health
CompanyPedestal Health
CategoryScience & Research
LocationResearch Triangle Park
RemoteOn-site (inferred)
EmploymentNot stated
LevelLead
SalaryNot stated by the employer
Posted9 Apr 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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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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