Director, Oncology Data Science
Merck
June 24, 2026
On-site
Boston, MA
Clinical Research and Development
The Translational Genome Analytics group is recruiting a Director of Oncology Data Scientist to perform data mining of multi-modal tumor profiling datasets to inform decisions across the oncology pipeline.
Responsibilities:
- Enable reverse translation from large multi-modal clinical datasets to inform biomarker discovery and combination strategies.
- Analyze, summarize, and visualize findings from multi-modal clinico-genomic datasets (bulk RNAseq, WES/WGS, imaging, epigenetic profiling, single-cell RNAseq, proteomics) from oncology clinical trials and real-world datasets.
- Use deep learning/AI and multivariate predictive modeling to discover novel biomarkers predictive of clinical outcomes.
- Collaborate with AIML teams developing foundation models trained on large cohorts of human data.
- Present analyses to stakeholders across early discovery, translational, and clinical development.
Qualifications:
- Ph.D. in a quantitative discipline (Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology, or related) with significant computational/statistical component; 6 years relevant experience in pharma/biotech/academia.
Required Experience and Skills:
- Computational methods experience in cancer biology.
- Expertise in statistical learning/data mining for integrative analysis of multimodal, high-dimensional tumor profiling datasets (oncology/immuno-oncology).
- Hands-on ML experience on large clinico-genomic/immunogenomic real-world and clinical trial datasets.
- Ability to code in scientific computation environments with reproducible-data-analysis best practices.
- Strong communication/presentation skills and data-driven hypothesis development; attention to detail.
- Independent, flexible, collaborative mindset.
Preferred Skills:
- Large-scale multi-modal data analysis (clinical trials and real-world).
- Deep understanding of cancer biology in molecular/imaging data.
- Matrixed industry collaboration experience.
- Publishing record in high-profile scientific journals.
Eligible Skills (listed): Cancer Genomics, Cancer Research, Computational Methods, Data Science, High Dimensional Data Analysis, Immuno-Oncology, Machine Learning (ML), Multimodal, Multimodal Analysis, Oncology, Real World Data, Statistical Learning.
Application:
- Apply via https://jobs.merck.com/us/en (deadline stated on posting).
Responsibilities:
- Enable reverse translation from large multi-modal clinical datasets to inform biomarker discovery and combination strategies.
- Analyze, summarize, and visualize findings from multi-modal clinico-genomic datasets (bulk RNAseq, WES/WGS, imaging, epigenetic profiling, single-cell RNAseq, proteomics) from oncology clinical trials and real-world datasets.
- Use deep learning/AI and multivariate predictive modeling to discover novel biomarkers predictive of clinical outcomes.
- Collaborate with AIML teams developing foundation models trained on large cohorts of human data.
- Present analyses to stakeholders across early discovery, translational, and clinical development.
Qualifications:
- Ph.D. in a quantitative discipline (Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology, or related) with significant computational/statistical component; 6 years relevant experience in pharma/biotech/academia.
Required Experience and Skills:
- Computational methods experience in cancer biology.
- Expertise in statistical learning/data mining for integrative analysis of multimodal, high-dimensional tumor profiling datasets (oncology/immuno-oncology).
- Hands-on ML experience on large clinico-genomic/immunogenomic real-world and clinical trial datasets.
- Ability to code in scientific computation environments with reproducible-data-analysis best practices.
- Strong communication/presentation skills and data-driven hypothesis development; attention to detail.
- Independent, flexible, collaborative mindset.
Preferred Skills:
- Large-scale multi-modal data analysis (clinical trials and real-world).
- Deep understanding of cancer biology in molecular/imaging data.
- Matrixed industry collaboration experience.
- Publishing record in high-profile scientific journals.
Eligible Skills (listed): Cancer Genomics, Cancer Research, Computational Methods, Data Science, High Dimensional Data Analysis, Immuno-Oncology, Machine Learning (ML), Multimodal, Multimodal Analysis, Oncology, Real World Data, Statistical Learning.
Application:
- Apply via https://jobs.merck.com/us/en (deadline stated on posting).