Principal Scientist or Assoc Director, Bioinformatics / Machine Learning
About The Position
We are seeking a highly experienced Principal Scientist/Associate Director (Princ Sci/Assoc Dir) to lead the application of machine learning and AI to large‑scale proteomics data in support of radiopharmaceutical target validation, prioritization, and patient selection strategies. This role will also have scientific ownership of the internal sample and proteomics data infrastructure, ensuring data quality, consistency, and long‑term usability across discovery and clinical programs. The Princ Sci/Assoc Dir will operate at the intersection of computational biology, proteomics, and translational oncology, transforming complex datasets into actionable insights that directly impact both preclinical and clinical decision‑making.
Requirements
- Ph.D. in Computer Science / Machine Learning or similar field with relevant experience (industry experience preferred)
- Hands‑on experience with biological data infrastructure, including sample and omics data management
- Proven use of public biological databases
- Deep understanding and expertise of Machine Learning Principles and how they apply to different models
- Proficiency in R and/or Python’s deep learning libraries
- Familiarity with multimodal data integration, including early and/or late fusion strategies.
- ML applied to Omics data (e.g., Proteomics, RNA-seq, DNA methylation), biological imaging modalities (e.g. microscopy, H&E, IF), and/or spatial biology.
Nice To Haves
- Experience with multi-GPU and distributed training at scale
- Experience analyzing large‑scale proteomics (LC‑MS/MS) datasets
- Experience in oncology drug discovery or translational research
- Familiarity with membrane proteins, GPCRs, or surface‑targeted therapeutics
- Experience supporting target validation, biomarker development, or clinical study design
Responsibilities
- Create and manage the central sample database integrated with the internal proteomics database, including the definition and implementation of data standards, schemas, and governance practices
- Integrate internal proteomics and sample databases with public resources ensuring harmonization between internal and public datasets
- Apply supervised, unsupervised, and semi‑supervised learning approaches for high-dimensional proteomics data
- Design and implement machine learning models using quantitative LC‑MS/MS proteomics data to: (1) identify biologically meaningful patient subgroups; (2) derive protein signatures predictive of target expression, uptake, and response; and (3) support target validation, prioritization, and indication selection
- Collaborate with translational and clinical teams to align analytical outputs with clinical study objectives
- Develop proteomics‑based patient selection signatures to: (1) identify responder‑enriched patient populations; (2) inform inclusion/exclusion criteria for clinical trials; and (3) support potential companion diagnostic strategies
- Develop models evaluating tumor selectivity versus normal and critical organs, and expression stability across disease stages and metastatic sites
- Maintain a work environment focused on scientific integrity and quality
- Perform other duties as required by business needs
Benefits
- bonus opportunity
- equity
- medical, dental, vision, life, short-term, and long-term disability insurance
- 401(k) retirement plan with employer match
- 4 weeks of paid time off (PTO) annually
- generous paid holidays
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
Ph.D. or professional degree
