Senior Staff Scientist, Quantitative Disease Modeling

Guardant Health

2d•Hybrid

About The Position

Guardant Health is a leading precision oncology company focused on guarding wellness and giving every person more time free from cancer. Founded in 2012, Guardant is transforming patient care and accelerating new cancer therapies by providing critical insights into what drives disease through its advanced blood and tissue tests, real-world data and AI analytics. Guardant tests help improve outcomes across all stages of care, including screening to find cancer early, monitoring for recurrence in early-stage cancer, and treatment selection for patients with advanced cancer. For more information, visit guardanthealth.com and follow the company on LinkedIn, X (Twitter) and Facebook. Guardant Health is seeking a Senior Staff Scientist, Quantitative Disease Modeling to join our AI & Translational Medicine organization. This senior individual contributor will define and lead the development of advanced simulation and disease models that evaluate the clinical and population-level impact of Guardant’s technologies across the cancer continuum. The role focuses on building robust quantitative frameworks to project disease progression, assess screening and monitoring strategies, and estimate long-term outcomes under alternative clinical scenarios, informing program strategy and external scientific engagement. This position requires deep expertise in multistate and natural history modeling, survival analysis, and simulation methods. The successful candidate will set modeling direction, independently design fit-for-purpose approaches, integrate diverse data sources, and apply rigorous statistical methods to quantify uncertainty and support model validation, review, and governance. In partnership with the Director of Health Economics & Decision Modeling, this individual will drive quantitative strategy across major Guardant programs and contribute to portfolio-level modeling decisions. This role carries significant scientific responsibility, cross-functional influence, and is expected to provide technical leadership and mentorship to other modelers.

Requirements

PhD in Statistics, Biostatistics, Applied Mathematics, Epidemiology, Operations Research, or related quantitative discipline.
10+ years of relevant experience (or 8+ years with exceptional depth in disease/natural history modeling and demonstrated scientific leadership).
Demonstrated expertise in developing, calibrating, validating, and interpreting complex disease models, including:
Multistate disease modeling
Microsimulation or individual-level simulation modeling
Natural history modeling
Survival analysis, long-term extrapolation, and competing risks
Model calibration, validation, and model risk/assumption management
Uncertainty quantification (deterministic and probabilistic sensitivity analysis)
Strong programming skills (e.g., R, Python, C++, Julia, or similar) and experience building reproducible, well-tested modeling workflows.
Proven ability to independently lead end-to-end modeling efforts, set methodological direction, and influence decisions with senior cross-functional stakeholders.
Excellent scientific communication skills, including the ability to clearly explain assumptions, limitations, and uncertainty to both technical and non-technical audiences.
Track record of peer-reviewed publications and/or high-impact scientific contributions in quantitative modeling or applied statistical methodology.

Nice To Haves

Experience in cancer screening, early detection, recurrence modeling, and/or surveillance strategies.
Experience evaluating detection bias, lead-time effects, and/or overdiagnosis in screening contexts.
Demonstrated ability to provide technical leadership and mentorship (e.g., guiding junior scientists/modelers, reviewing analytic work, establishing best practices).
Experience translating complex quantitative analyses into decision-ready insights for product, clinical, medical affairs, and/or commercial stakeholders.
Familiarity with decision-analytic modeling and/or interfaces with health economics/HTA (e.g., linking disease models to cost-effectiveness or value frameworks).
Prior collaboration with interdisciplinary clinical, translational, and biostatistics teams; experience incorporating RWD and clinical trial evidence into models.
Experience authoring and presenting scientific content externally (publications, conferences) and contributing to responses for external scientific scrutiny.
Experience evaluating detection bias or overdiagnosis in screening contexts.
Prior collaboration with interdisciplinary clinical and translational teams.

Responsibilities

Lead Disease & Simulation Modeling Across the Cancer Continuum
Design, develop, and validate advanced quantitative models, including microsimulation, multistate, and natural history models.
Represent disease progression across clinically relevant states, including screening, diagnosis, recurrence, metastasis, and survival.
Project long-term outcomes under alternative screening, surveillance, and treatment strategies.
Evaluate detection bias, lead-time effects, and overdiagnosis in screening and early detection contexts.
Conduct model calibration using epidemiologic, clinical trial, and real-world data inputs.
Perform deterministic and probabilistic sensitivity analyses.
Assess structural uncertainty and alternative modeling assumptions.
Apply Rigorous Statistical Methods to Complex Disease Questions
Translate clinical and epidemiologic evidence into defensible model parameters.
Lead survival modeling and long-term extrapolation analyses.
Quantify and propagate uncertainty throughout simulation frameworks.
Conduct evidence synthesis when needed to inform model inputs.
Ensure transparency, reproducibility, and technical rigor in modeling workflows.
Contribute to Strategic Decision Support
Develop decision-analytic frameworks comparing alternative diagnostic and monitoring strategies.
Quantify downstream clinical impact, including changes in stage distribution, recurrence risk, and survival outcomes.
Collaborate with health economics colleagues to extend clinical models to value-based applications when required.
Provide quantitative insight to support internal strategy and external scientific discussions.
Provide Scientific Leadership
In partnership with the Director, define and drive modeling strategy within key programs across the cancer continuum.
Develop and refine methodological approaches when new scientific questions require innovative solutions.
Establish modeling standards and validation practices to ensure consistency and credibility across projects.
Provide technical leadership to cross-functional teams on complex quantitative issues.
Contribute to peer-reviewed publications and scientific presentations.
Support Clinical Program Design (Secondary)
Provide input on study endpoints and follow-up considerations to ensure compatibility with long-term modeling needs.
Collaborate with clinical and biostatistics teams where modeling assumptions intersect with trial design.