Senior Quantitative Scientist

About Pedestal Health

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.

The Role

In this role, you will be a foundational member of Pedestal Health’s Quantitative Sciences (QS) organization, helping to establish Pedestal as a leader in rigorous, reproducible, and regulatory-grade evidence generation. You will serve as a scientific owner of Pedestal’s data, developing deep expertise in its structure, quality, and applications while helping to build scalable data validation, curation, and analytic processes that ensure reliability and usability.

Working closely with cross-functional partners in Product, Engineering, Clinical Operations, and Medical Science, you will design and execute analyses that demonstrate the value of Pedestal’s data, support customers across life sciences, regulatory, and academic settings, and build scalable, standardized analytic approaches for real-world evidence generation.

What You'll Do

Data Ownership and Quality 

• You will establish Pedestal Health’s data as a trusted, validated scientific asset.
• Ensure Pedestal’s data are accurate, complete, and fit for regulatory and scientific use by building systems and practices that establish trust in data integrity and readiness for analysis.
• Develop deep expertise in Pedestal’s data, understanding its structure, provenance, strengths, and limitations.
• Design and implement validation frameworks, data quality checks, and “fitness-for-use” assessments.
• Create and maintain data dictionaries and documentation that promote transparency and reproducibility.

Analytical excellence 

• You will apply advanced analytic methods and scientific rigor to generate reliable, reproducible, and regulatory-grade real-world evidence from Pedestal’s data assets.
• Support the design and execution of retrospective and prospective studies using Pedestal’s data assets.
• Develop statistical analysis plans and analytic code for descriptive, comparative, and predictive analyses.
• Design and implement scalable, reusable analytic frameworks and modular code structures that support consistent execution across therapeutic areas and data products.
• Apply best practices in methodology, version control, code review, and documentation to ensure reproducibility.
• Translate complex results into clear visualizations and insights for scientific, regulatory, and client audiences.
• Contribute to publications, abstracts, and collaborations that advance Pedestal’s scientific reputation and influence.

Cross functional partnership

• You will work cross-functionally to connect data, science, and strategy, ensuring Pedestal’s evidence generation aligns with customer needs and business goals.
• Partner closely with Commercial, Medical Science, Product, Data Insights, Engineering, and Clinical Operations teams to align on data strategy and analytic priorities.
• Serve as the internal “voice of the customer,” anticipating how clients, regulators, and researchers will use Pedestal’s data.
• Participate in client and partner meetings to demonstrate the validity and value of Pedestal’s data products.
• Leverage your deep knowledge of Pedestal’s data assets to guide internal teams and external partners, anticipating analytical and operational challenges and helping to shape data use strategies.
• Translate analytic findings and data characteristics into clear, client-facing materials that demonstrate Pedestal’s data quality, representativeness, and scientific utility.

What You'll Bring

• Advanced degree (MS or PhD) in biostatistics, epidemiology, health economics, bioinformatics, data science, or a related quantitative field. 
• 5-10+ years of experience working with real-world data (e.g., EMR, claims, registry, or clinical trial data).
• Strong programming expertise in R, and experience with reproducible analytic frameworks.
• Proven expertise in data quality assessment, validation, and curation workflows.
• Demonstrated ability to build and maintain reproducible coding practices and scalable analytic processes that enable efficiency, transparency, and consistency across studies.
• Strong communication, presentation and collaboration skills with cross-functional technical and scientific teams.
• Demonstrated ability to translate data insights into actionable scientific or business recommendations.