Associate Director, Data Science, Computational Oncology

Role: Associate Director of Data Science in Computational Oncology, focused on applying quantitative and statistical methods to cancer genomics data. Responsibilities include analyzing large-scale multi-omics datasets (RNA-seq, WES/WGS, scRNA-seq), functional genomics screens (CRISPR, Perturb-seq), and integrating data with biological knowledge to identify targets and biomarkers across oncology and immunology programs. The role involves developing machine learning and deep learning models, utilizing AI tools like LLMs, and collaborating with experimental and computational teams to translate data into actionable insights for drug discovery. Requirements: PhD in bioinformatics, computational biology, biostatistics, or related STEM field with 0+ years of experience (or MS with 8+ years, BS with 12+ years); strong expertise in NGS data analysis, statistical modeling, machine learning, and programming (R, Python, Linux, cloud platforms). Preferred: postdoctoral or industry experience in cancer genomics or computational oncology, experience with functional genomics, foundation models, and network-based pathway analysis, along with a publication record. HighValue: tumor biology, cancer genomics, multi-omics, functional genomics, AI/ML, therapeutic areas include oncology and immunology, hybrid onsite in Boston with up to 10% travel.