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Postdoctoral Scientist, Oncology Foundation Models
Johnson & Johnson. Join the Data, Data Science & AI organization within Johnson & Johnson Innovative Medicine .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in developing and evaluating computational models for oncology using large-scale omics and clinical datasets, with strong programming skills in Python and experience in machine learning and deep learning methodologies. Proven ability to analyze high-dimensional biological data and communicate complex concepts effectively to diverse audiences.
Highest-signal resume keywords
Ph.D. In Computer Science, Artificial Intelligence, Bioinformatics, Computational Biology, Or StatisticsStrong Programming Skills In PythonExperience With Machine Learning And Deep LearningExperience Analyzing Large-Scale Transcriptomic DatasetsStrong Written And Verbal Communication Skills
ATS Keywords
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Hard Skills
Machine LearningDeep LearningStatistical ModelingSingle-Cell RNA-SeqSpatial TranscriptomicsModular Software DevelopmentData AnalysisModel EvaluationFoundation ModelsTransformers
Soft Skills
Strong Communication SkillsCollaboration
Tools & Technologies
PythonR
Industry Keywords
OncologyCancer BiologyDrug DiscoveryComputational BiologyTranslational Research
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Join the Data, Data Science & AI organization within Johnson & Johnson Innovative Medicine
- Contribute to developing and evaluating foundation models for oncology using large-scale omics and clinical datasets
- Leverage single-cell, spatial, and other high-dimensional molecular data to understand tumor biology, the tumor microenvironment, and clinically relevant disease mechanisms
- Collaborate with Oncology Translational Research to develop computational models supporting precision medicine and drug development
- Analyze and integrate high-dimensional biological data, including single-cell and spatial transcriptomics, for model development, training, and validation
- Develop and implement scalable statistical, machine learning, and deep learning approaches using large-scale transcriptomic and multimodal datasets
- Design model evaluation strategies with benchmarks, validation datasets, and biologically relevant performance metrics
- Partner with experimental and translational scientists to design studies evaluating and interpreting computational predictions
- Develop reproducible, modular, and scalable research code meeting internal software engineering and data integrity standards
- Communicate methods, model performance, and scientific findings through technical reports, presentations, and peer-reviewed publications
Requirements
What you’ll need- Ph.D. completed within the last three years in computer science, artificial intelligence, bioinformatics, computational biology, statistics, or a related quantitative field
- Strong programming skills in Python
- Experience with R is desirable
- Strong background in machine learning, deep learning, statistical modeling, or representation learning
- Experience developing modular, scalable, and reusable software for computational research or AI/ML applications
- Experience analyzing large-scale transcriptomic or other high-dimensional biological datasets
- Demonstrated ability to develop and rigorously evaluate computational models
- Strong research record, including peer-reviewed publications
- Strong written and verbal communication skills, with the ability to communicate complex computational concepts to technical and scientific audiences
- Preferred: experience applying AI/ML methods to oncology, cancer biology, or drug discovery
- Preferred: experience with single-cell RNA-seq, spatial transcriptomics, or multimodal biological datasets
- Preferred: experience with foundation models, representation learning, transformers, or other large-scale deep learning architectures for biological data
- Preferred: familiarity with cell therapy discovery and development
- Preferred: experience at the interface of computational modeling and experimental or translational biology
Benefits
Comp & perks- Annual bonus with set target depending on pay grade/location, based on employee and company performance
- Vacation days
- Parental leave for a minimum of 12 weeks
- Bereavement leave
- Caregiver leave
- Volunteer leave
- Well-being reimbursement
- Financial, physical, and mental health programs
- Service anniversary and recognition awards
- Insurance plans for employees and, in some locations, eligible dependents