Principal, Quantitative Sciences

Target RWE•Research Triangle Park, NC

9h•Hybrid

About The Position

At Target RWE, our mission is driven by a deep commitment to people, whether it’s the patients we serve, the partners we collaborate with, or the dedicated team members who power our work. As a purpose-driven organization, we leverage real-world data to advance clinical research and inform better healthcare decisions. We foster a collaborative environment where every voice is heard, every idea has an impact, and every contribution helps improve lives. If you're seeking a place where your work truly matters, join us to advance science. Overview We are seeking a Principal to join Target RWE’s Quantitative Sciences (QS) organization. In this role, you will help shape how we design, execute, and communicate real-world clinical studies using Target’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 Target’s scientific capabilities, engaging directly with pharmaceutical and biotech partners, supporting client discussions, and helping translate complex analytical approaches into clear, actionable insights.

Requirements

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 support exploratory work, validation, and collaboration with analytic teams
Experience working directly with clients or external partners in life sciences or healthcare
Excellent communication and presentation skills, with the ability to explain complex concepts clearly
Comfortable operating in client-facing settings, including meetings, presentations, and scientific discussions
Strong collaboration skills across technical and non-technical teams

Responsibilities

Ensure that Target’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
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 Target’s scientific approach and data value
Represent Target in external settings, including conferences, scientific collaborations, and partner discussions
Build trust with clients by providing thoughtful, pragmatic, and credible scientific guidance
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