Associate Director, Data Science, Functional Genomics

Associate Director of Data Science, Functional Genomics (R4) Translational Genome Analytics (TGA) / Data, AI and Genome Sciences (DAGS) Location Cambridge, MA Our company is a global health care leader committed to being the world’s premier research intensive biopharmaceutical company. Our Research Laboratories will take our leading discovery capabilities and world class small molecule and biologics research and development expertise to create breakthrough science that radically changes how we approach serious diseases. The Data, AI and Genome Sciences (DAGS) department seeks a talented computational biologist for our Translational Genome Analytics (TGA) team. In this role, you will lead our Functional Genomics & Predictive Modeling function, shaping how its evidence is generated, interpreted, and integrated across the discovery portfolio. You will own the computational and modeling frameworks that range from hit calling for our perturbational screens to multi-evidence integration to prioritize targets and/or drug combinations. You will serve as a technical and scientific leader who shapes early discovery direction, mentors a team of scientists, and applies cutting edge AI and ML to accelerate how we turn data into decisions. This is a rare opportunity to build and lead a functional genomics analytics capability from the ground up, where your team's calls directly shape which targets advance in our discovery portfolio, one of the most exciting frontiers in computational biology today. In This Exciting Role You Will Lead the design and build of scalable computational analytics frameworks for pooled, arrayed, single cell, and optical CRISPR screens, from QC pipelines and library design to longitudinal readout analysis. Invent and scale computational methods for the next generation of functional genomics, spanning scalable single cell perturbation screening, cellular barcoding, and lineage tracing, to elucidate adaptive resistance mechanisms and drug combinations. Build image analysis pipelines for high content and optical CRISPR screens, turning morphological phenotypes into biological insight that guides target prioritization. Integrate functional genomics and imaging derived results with high throughput transcriptomics and proteomics datasets to build multi evidence target prioritization packages for multiple stages of drug discovery. Bring modern AI and ML, including LLM powered agentic workflows and network based methods, to how we triage targets and synthesize biological evidence. Lead and mentor a team of scientists, set the technical direction for functional genomics analytics, and drive standards for reproducible research and FAIR data infrastructure. Collaborate across disciplines with experimental scientists, software engineers, and external partners to advance shared analytical platforms and support Therapeutic Area target identification.

MS in computational biology, bioinformatics, biostatistics, biophysics, mathematics, statistics, genetics/genomics, computer science or a related STEM discipline and a minimum of 8 years of relevant professional experience, including hands on experience analyzing large scale NGS and functional genomics datasets. A passion for solving biological problems through computational methods with a proactive focus on details and execution. Experience with the computational analysis, algorithm development, and biological interpretation of large scale NGS and functional genomics datasets. A proven track record of applying machine learning to analyze single cell RNA sequencing data to identify novel patterns and functional insights. Previous experience with experimental design of biological assays, statistical hypothesis testing, and integrating results from multiple omics data sources. Proficiency in at least one statistical programming language such as R or Python, along with experience using version control environments like Git. Familiarity with public