Director, Data Science, Functional Genomics

Job Description

Director of Data Science, Functional Genomics

Group 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 an accomplished computational biologist for our Translational Genome Analytics (TGA) team. In this role, you will lead our functional genomics analytics 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 senior technical and scientific leader who sets early discovery direction, builds and leads 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.
• ·Build, lead, and grow a team of scientists, set the strategic and 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.

Minimum Requirements

• A Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Statistics, Computer Science, Mathematics, or a related field.
• A minimum of 10 years of relevant experience in computational biology, bioinformatics, biostatistics, or a related quantitative discipline, including hands on experience analyzing large scale NGS and functional genomics datasets.
• Demonstrated experience leading and building analytics teams, setting technical strategy, and mentoring scientists.
• 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 diverse 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 data repositories like The Cancer Genome Atlas, Dependency Map, Cancer Cell Line Encyclopedia, and Clinical Proteomic Tumor Analysis Consortium.
• Experience with AWS cloud computing infrastructure and Linux environments.
• Excellent oral and written communication skills.

Preferred Experience and Skills

• A strong background with post-doctoral or relevant industry experience, including experience leading multidisciplinary teams or functions and influencing scientific strategy across an organization.
• Substantial computational experience specifically with functional genomics data, including CRISPR screen hit calling frameworks and library design interpretation.
• Experience with optical pooled CRISPR screening image analysis pipelines and integrating morphological readouts with genomic datasets.

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