Lead Data Scientist, Computational Oncology & Genomics

MSD Malaysia; Boston (MA); On-site; USD 160,000 - 251,000; Full time

Job summary

Merck & Co., Inc. in Boston seeks an Associate Director of Data Science in Computational Oncology (R4) within Translational Genome Analytics. You will lead analyses of large oncology datasets, nominate targets, and guide biomarker discovery to advance cancer programs.

You will apply statistics, ML, and deep learning to multi-omics data and collaborate with experimental and AI teams to translate findings into program decisions.

Qualifications

• PhD or equivalent in a quantitative STEM field with substantial oncology/genomics experience.
• Proven track record analyzing large-scale NGS datasets and integrating multi-omics data.
• Strong statistics knowledge with ML applications in biology and cancer.
• Proficient in R or Python, Git, Linux, and cloud/data platforms.

Responsibilities

• Analyze large-scale oncology datasets (bulk RNA-seq, WES/WGS, scRNA-seq) to understand biology.
• Nominate targets and biomarkers; prioritize drug combinations using multi-omics and functional genomics evidence.
• Apply ML, deep learning, and foundation models to biological problems; ensure reproducible research.
• Collaborate across AI/ML, biology, and program teams to translate findings into programs.

Skills

Statistical analysis

Machine learning

Deep learning

Omics data analysis

R / Python

Git

Linux

AWS / S3

Nextflow

Databricks

Posit

Education

PhD in computational biology / biostatistics / genomics

MS in related field

BS with 12+ years experience

Tools

R

Python

Git

Linux

AWS

S3

Nextflow

Databricks

Posit

Job description

Merck & Co., Inc. in Boston seeks an Associate Director of Data Science in Computational Oncology (R4) within Translational Genome Analytics. You will lead analyses of large oncology datasets, nominate targets, and guide biomarker discovery to advance cancer programs.

You will apply statistics, ML, and deep learning to multi-omics data and collaborate with experimental and AI teams to translate findings into program decisions.