Director, Oncology Data Science
Merck
July 01, 2026
Remote friendly (Boston, MA)
United States
IT
The Successful Candidate Will
- Enable reverse translation from large multi-modal clinical datasets to inform biomarker discovery and combination strategies in molecularly defined patient populations.
- Analyze, summarize, and visualize findings from multi-modal clinico-genomic datasets (bulk RNAseq, WES/WGS, imaging, epigenetic profiling, single-cell RNAseq, proteomics) from oncology trials and real-world datasets.
- Use advanced 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 teams.
Qualifications
- Ph.D. in a quantitative discipline (e.g., Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology) with significant computational/statistical component and 6 years of relevant experience in pharma/biotech/academia.
Required Experience and Skills
- Computational methods experience in cancer biology.
- Expertise in statistical learning and data mining for integrative analysis of multimodal, high-dimensional tumor profiling datasets (oncology/immuno-oncology).
- Hands-on ML on large clinico-genomic/genetic/immunogenomic real-world and clinical trial datasets.
- Ability to code in scientific computation environments with best practices for reproducible analyses.
- Strong communication/presentation; ability to guide decisions with data-driven hypotheses; attention to detail.
- Independent, flexible, and collaborative mindset.
Preferred Experience and Skills
- Experience analyzing large-scale multi-modal clinical trial and real-world data.
- Deep understanding of cancer biology as represented in multi-modal molecular and imaging data.
- Experience in matrixed industry environments.
- Record of publishing in high-profile scientific journals.
Required Skills
- Cancer Genomics, Cancer Research, Computational Methods, Data Science, High Dimensional Data Analysis, Immuno-Oncology, Machine Learning (ML), Multimodal Analysis, Oncology, Real World Data, Statistical Learning.
Application Instructions
- Apply via https://jobs.merck.com/us/en (or Workday Jobs Hub as a current employee). Application deadline is stated on the posting.
- Enable reverse translation from large multi-modal clinical datasets to inform biomarker discovery and combination strategies in molecularly defined patient populations.
- Analyze, summarize, and visualize findings from multi-modal clinico-genomic datasets (bulk RNAseq, WES/WGS, imaging, epigenetic profiling, single-cell RNAseq, proteomics) from oncology trials and real-world datasets.
- Use advanced 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 teams.
Qualifications
- Ph.D. in a quantitative discipline (e.g., Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology) with significant computational/statistical component and 6 years of relevant experience in pharma/biotech/academia.
Required Experience and Skills
- Computational methods experience in cancer biology.
- Expertise in statistical learning and data mining for integrative analysis of multimodal, high-dimensional tumor profiling datasets (oncology/immuno-oncology).
- Hands-on ML on large clinico-genomic/genetic/immunogenomic real-world and clinical trial datasets.
- Ability to code in scientific computation environments with best practices for reproducible analyses.
- Strong communication/presentation; ability to guide decisions with data-driven hypotheses; attention to detail.
- Independent, flexible, and collaborative mindset.
Preferred Experience and Skills
- Experience analyzing large-scale multi-modal clinical trial and real-world data.
- Deep understanding of cancer biology as represented in multi-modal molecular and imaging data.
- Experience in matrixed industry environments.
- Record of publishing in high-profile scientific journals.
Required Skills
- Cancer Genomics, Cancer Research, Computational Methods, Data Science, High Dimensional Data Analysis, Immuno-Oncology, Machine Learning (ML), Multimodal Analysis, Oncology, Real World Data, Statistical Learning.
Application Instructions
- Apply via https://jobs.merck.com/us/en (or Workday Jobs Hub as a current employee). Application deadline is stated on the posting.