Senior Scientist / Principal Scientist, Clinical Computational Biology

Job Summary

Vividion is seeking a Senior or Principal Scientist, Clinical Computational Biology, to transform clinical data into actionable insights for our oncology and immunology programs. The Senior Scientist will analyze data from ongoing clinical trials, mine publicly available clinical trial datasets, and support reverse translational efforts that translate clinical findings back into discovery and preclinical research. They will also build tools to aid exploration and visualization of clinical data in a controlled environment.

The ideal candidate will bring deep expertise in cancer biology and/or immunology, a strong clinical and translational background, and the computational skills needed to transform complex patient data into usable biological insights. They will analyze and interpret biomarker measurements in the context of patient biology, drug exposure, biological response and safety, often in small cohorts with imperfect data. They must be able to communicate findings, limitations, and uncertainties clearly to clinicians, biologists, and program leadership.

The Senior Scientist will report to the Head of Computational Biology, with a dotted-line reporting relationship to the Head of Clinical Translation. They will work independently as part of a cross-disciplinary team within the Computational Biology group and Translational team, and partnering closely with Clinical Development, and Biology.

Essential Duties and Responsibilities

• Analyze biomarker and pharmacodynamic data (e.g., proteomic, transcriptomic, genomic) from Vividion's ongoing clinical trials. Relate molecular changes to dose, exposure, response and safety, and translate the results into biological and clinical insight.
• Mine publicly available clinical trial and patient-level datasets to benchmark Vividion's programs, contextualize clinical observations, and inform target and indication strategy.
• Drive reverse translational research by connecting clinical findings to preclinical models, discovery biology, and testable biological hypotheses.
• Build and maintain interactive tools for exploring and visualizing clinical data in a controlled-access environment, working with IT and Clinical Data Management to meet privacy and compliance requirements.
• Partner with Translational Medicine, Clinical Development, and Biology to shape biomarker strategy, including candidate selection, assay prioritization and data-driven hypotheses for upcoming studies.
• Communicate results clearly to clinicians, program teams and leadership. Contribute to data packages and decision-making and add reproducible code to a shared codebase.

Education and Experience Requirements

• Ph.D. in Cancer Biology, Immunology, Systems Biology, Computational Biology or a related discipline with 3+ years of relevant industry experience; M.S. with 8+ years of relevant industry experience will also be considered.
• Demonstrated experience interpreting human clinical or translational datasets (e.g., clinical trial, cohort or patient-derived data), linking molecular measurements to disease biology, drug mechanism and clinical outcomes.
• Working statistical literacy, including regression, survival analysis and multiple-testing control, and an understanding of the pitfalls of small clinical cohorts and missing data.
• Proficiency in R and Python and in a UNIX/Linux environment; experience with reproducible workflows and version control.
• Hands-on experience with omics data (e.g., RNA-seq, proteomics, genomics); single-cell experience is a plus.
• Experience building interactive data applications (e.g., R Shiny, Dash) or comparable visualization tools for scientific users.

Preferred Qualifications

• Experience in drug discovery or development, including oncology and/or immunology clinical data and biomarker-driven trials.
• Familiarity with clinical data standards and structures (e.g., CDISC SDTM/ADaM) and with data from clinical data management systems.
• Experience with public clinical and genomic resources (e.g., TCGA, cBioPortal, GEO, ClinicalTrials.gov, published trial datasets) and with reverse translation or translational biomarker work.
• Experience with pharmacokinetic/pharmacodynamic or exposure-response analysis.
• Experience handling sensitive clinical data under privacy, access-control and compliance requirements (e.g., HIPAA, GCP awareness).
• Experience deploying and maintaining applications on a shared server or cloud (e.g., Azure) environment.

Required Key Attributes

• Strong foundation in disease biology and clinical medicine, and a passion for patient-focused scientific discovery.
• Excellent oral and written communication skills, including the ability to explain complex analyses to clinicians, biologists and other non-experts.
• Effective troubleshooting and problem-solving skills.
• An effective team player and strong individual contributor: flexible, able to work independently and in cross-functional teams, and able to handle multiple tasks and deliver high-quality results under tight timelines.