Associate Director, Data Science, Computational Oncology

Associate Director of Data Science position within the Translational Genome Analytics team, focused on applying statistical and machine learning methods to multi-omics and functional genomics datasets to advance oncology drug discovery and target nomination.

Responsibilities

• Analyze large-scale oncology datasets to understand disease biology, progression mechanisms, and drug action
• Analyze functional genomics screens including CRISPR and Perturb-seq from QC through biological interpretation to establish target dependency
• Nominate and prioritize drug combinations by integrating expression profiling, disease biology, mechanistic evidence, and competitive landscape
• Apply statistics and machine learning to multi-omics data integration with biological knowledge using network-based approaches
• Collaborate across experimental scientists, AI/ML teams, software engineers, and program teams to advance discovery oncology research

Requirements

• Deep expertise analyzing and interpreting large-scale NGS datasets including bulk RNA-seq, WES/WGS, and scRNA-seq with experimental design and QC knowledge
• Strong statistical foundation spanning hypothesis testing, regression, survival analysis, dimensionality reduction, with machine learning application to biological data
• Proficiency in R or Python, Git version control, Linux, and cloud platforms such as AWS, Nextflow, Databricks, Posit
• Familiarity with major cancer genomics resources including TCGA, GTEx, CCLE, CPTAC, and DepMap
• Collaborative working style with excellent oral and written communication abilities
• Demonstrated passion for solving oncology and cancer genomics problems with attention to detail