Associate Director, Functional Genomics, Data Science

Job Description

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 to join our Translational Genome Analytics (TGA) team in Cambridge, MA. In this role, you will lead the functional genomics analytics function, owning the computational infrastructure, hit calling frameworks, and analytical tooling that underpin our CRISPR screening programs. You will serve as a technical and scientific leader, shaping how functional genomics evidence is generated, interpreted, and integrated with orthogonal data sources to inform early discovery decisions and support our discovery portfolio. You will also oversee and directly contribute to the computational analysis of optical CRISPR screens and high throughput imaging datasets, bridging morphological phenotypes with our functional genomics capabilities.

In This Exciting Role You Will

• Lead the design, development, and maintenance of scalable computational analytics frameworks for pooled, arrayed, single cell, and optical CRISPR screens, including QC pipelines, library design, and longitudinal readout analysis.

• Oversee and actively contribute to image analysis pipelines for high content and optical CRISPR screens, extracting biologically meaningful morphological features to support 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.

• Leverage cutting edge AI and ML approaches, including LLM powered agentic workflows and network based methods, to accelerate target triage and automate biological evidence synthesis.

• Manage and mentor PhD level scientists, set the technical direction for the analytics sub team, 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.

Education Minimum Requirement

• Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Statistics, Computer Science, Mathematics, Genetics/Genomics, or a related STEM field.

Required Experience and Skills

• A minimum of 4+ years of industry or applied academic experience.

• 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 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 prior experience leading an analytics team and mentoring scientists.

• 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.