Associate Director, Data Science, Computational Oncology

About the role

MSD's Data, AI and Genome Sciences (DAGS) department is looking for an Associate Director of Data Science, Computational Oncology (R4) to join its Translational Genome Analytics (TGA) team in Boston, MA, on a hybrid basis. The role brings rigorous statistics, machine learning and modern AI to large multi-omics and functional genomics datasets to understand the molecular basis of cancer and to nominate targets and biomarkers. It suits scientists with a strong oncology and cancer genomics background who can also analyze functional genomics screens.

Key responsibilities

• Analyze large-scale oncology datasets, including bulk RNA-seq, whole-exome and whole-genome sequencing, and single-cell RNA-seq, to understand disease biology and drug action.
• Analyze functional genomics and perturbational screens, including pooled and arrayed CRISPR screens and Perturb-seq, from QC and hit calling through biological interpretation.
• Nominate and prioritize drug combinations for oncology and immunology by weighing expression profiling, disease biology, mechanistic and functional-genomics evidence and the competitive landscape.
• Apply statistics and integrate multi-omics and functional genomics data with prior biological knowledge using network-based and machine learning approaches.
• Use deep learning and single-cell foundation models for cell-type deconvolution, batch integration, patient stratification and perturbation-response prediction.
• Use modern AI, including LLM-based agents and retrieval over internal data, to accelerate evidence synthesis.
• Drawing on human and real-world data, connect preclinical findings to patient biology.
• Collaborate with experimental scientists, AI and ML and data science teams, software engineers and program teams, and uphold reproducible, well-documented research.

Required profile

• PhD in a quantitative STEM field (e.g., computational biology, biostatistics, genetics, computer science) with 4+ years of relevant experience, or MS with 8+ years, or BS with 12+ years.
• Deep experience analyzing and biologically interpreting large NGS datasets (bulk RNA-seq, WES/WGS, scRNA-seq) and integrating omics layers.
• Strong statistical foundation covering hypothesis testing, regression and mixed models, survival analysis, dimensionality reduction, and machine learning applied to biological data.
• Strong technical skills, including R or Python, Git, Linux, and hands-on experience with AWS (S3), Nextflow, Databricks and Posit.
• Familiarity with cancer genomics resources such as TCGA, GTEx, CCLE, CPTAC and DepMap.
• Preferred: post-doctoral or industry experience in cancer genomics or computational oncology, substantial experience with CRISPR screen hit-calling and image-based phenotypic profiling, and a strong publication record.

What we offer

• Base salary range of $159,600 to $251,200 depending on experience.
• Up to 10% travel.