Principal Data Scientist - Multimodal AI Oncology
V
VeracyteBiotech, Diagnostics, Healthcare
Within USA or CanadaFull-TimePrincipal
Salary182000 - 208000 USD per year
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Job Details
- Experience
- Minimum 8 years of relevant experience, with at least 5 years in an industry setting
- Required Skills
- AWSPythonSQLMachine LearningR
Requirements
- Ph.D. in bioinformatics, computational biology, genomics, biostatistics, computer science, or a related field applying quantitative computational methodologies to biological/clinical problems.
- Minimum 8 years of relevant experience, with at least 5 years in an industry setting (biotech, diagnostics, or healthcare preferred).
- Demonstrated expertise in multimodal data integration, machine learning, and model development for NGS-based clinical diagnostics.
- Strong programming skills in Python, R, and SQL.
- Strong experience with cloud computing environments (AWS preferred).
- Deep knowledge of genomics, transcriptomics, digital pathology, and clinical data analysis.
- Proven track record of technical leadership, project ownership, and successful delivery of high-impact R&D projects.
- Excellent communication skills and ability to mentor and lead interdisciplinary teams.
- Strong publication record in peer-reviewed journals, including first and senior authorship.
Responsibilities
- Lead research into novel MMAI models while closely collaborating with other machine learning experts across the computational team on strategy, study design, cohort selection, data acquisition, and data generation.
- Architect, train, and validate MMAI models integrating modalities including genomics, transcriptomics, whole-slide imaging (e.g. H&E tumor tissue slides), and clinical features for cancer prognosis, risk stratification, diagnosis, and therapy selection.
- Drive proof-of-concept and feasibility projects from definition through model development, benchmarking, interpretation, and dissemination of results.
- Design and implement pipelines for ingesting, harmonizing, and integrating diverse data modalities (including whole-slide images, RNA-seq, WGS, clinical metadata).
- Work closely with wet lab scientists, bioinformatics/data science teams, medical/clinical/pathology teams, software/data/cloud engineers, and other cross-functional teams to ensure models are biologically interpretable and clinically applicable.
- Prepare and present findings to technical and non-technical audiences, including conference abstracts and presentations, scientific publications, and internal reports.
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