Associate Director, Functional Genomics, Data Science

About the role

The Data, AI and Genome Sciences (DAGS) department seeks a talented computational biologist to join our Translational Genome Analytics (TGA) team in Cambridge, MA.

Responsibilities

• Lead the functional genomics analytics function, owning the computational infrastructure, hit calling frameworks, and analytical tooling that underpin our CRISPR screening programs.
• 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.
• 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.
• Design, develop, and maintain 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.

Requirements

• 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.
  • Past experience 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.
  • Expertise applying deep learning approaches to image-based phenotypic profiling and cell classification for target identification.
  • Deep understanding of general disease biology and immunology, with knowledge of the latest functional genomics research.
  • Expertise in utilizing network-based analysis frameworks or transfer learning techniques to infer gene regulatory patterns from NGS datasets.
  • Hands-on experience building or deploying LLM-powered systems or AI tools for biological data interrogation.
  • Experience developing interactive data visualization tools, for example R Shiny, for multi-omics readouts.