Scientist, Computational Biology
Core
Develop computational pipelines and analytical frameworks to detect disease-relevant cellular dynamics from minimally invasive biospecimens for early disease detection and monitoring.
Role type
Senior IC computational biologist (bioinformatics/ML)
Builds
Core platform for liquid biopsy analysis and assay development
Domain
Life sciences / Liquid biopsies / Proteomics / Transcriptomics
Deliverable
production ML models
Required skills
computational pipelines, multi-modal data integration, statistical methods, machine learning, bioinformatics, experimental design, analytical frameworks, reproducible code, LLMs for scientific contextualization
Preferred skills
proteomics, transcriptomics, assay development, software engineering collaboration
Technologies
LLMs, internal proprietary assay data tools
Responsibilities
Develop, maintain, and scale computational pipelines for proteomics, transcriptomics, and internal proprietary assay data; Integrate multi-modal biological datasets to identify disease-relevant molecular patterns, candidate biomarkers, and assay features; Design and apply statistical, machine learning, and bioinformatics methods to improve assay sensitivity, specificity, reproducibility, and biological interpretability; Partner with biologists, assay developers, and leadership to design experiments, define success criteria, analyze results, and validate key biological and computational hypotheses; Collaborate with software and data engineers to build internal tools, dashboards, and user interfaces that enable scientists to explore, interpret, and pressure-test FL103 data; Build literature- and knowledge-based contextualization workflows, including responsible use of LLMs, to connect internal findings with external scientific evidence and disease biology; Develop rigorous analytical frameworks for comparing candidate markers, assay conditions, biological cohorts, and disease states; Ensure analyses are reproducible, well-documented, and version-controlled, with clear standards for data provenance, code quality, and interpretation
