Associate Director, Data Science, Functional Genomics

• Lead the design and build of scalable computational analytics frameworks for pooled, arrayed, single-cell, and optical CRISPR screens
• Develop QC pipelines, library design methods, and longitudinal readout analysis
• Invent and scale computational methods for single-cell perturbation screening, cellular barcoding, and lineage tracing
• Build image analysis pipelines for high-content and optical CRISPR screens
• Integrate functional genomics and imaging results with transcriptomics and proteomics datasets
• Build multi-evidence target prioritization packages across stages of drug discovery
• Apply AI and ML, including LLM-powered agentic workflows and network-based methods, to triage targets and synthesize biological evidence
• Lead and mentor scientists and set the technical direction for functional genomics analytics
• Drive standards for reproducible research and FAIR data infrastructure
• Collaborate with experimental scientists, software engineers, and external partners
• Support Therapeutic Area target identification

• MS in computational biology, bioinformatics, biostatistics, biophysics, mathematics, statistics, genetics/genomics, computer science or a related STEM discipline
• Minimum of 8 years of relevant professional experience, including hands-on experience analyzing large-scale NGS and functional genomics datasets
• Experience with computational analysis, algorithm development, and biological interpretation of large-scale NGS and functional genomics datasets
• Proven track record applying machine learning to single-cell RNA sequencing data
• Experience with experimental design of biological assays, statistical hypothesis testing, and integrating multiple omics data sources
• Proficiency in R or Python
• Experience with version control environments such as Git
• Familiarity with 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
• Up to 10% travel required
• Preferred:
  Ph.D. in a related STEM field with 4+ years of professional experience
• Preferred:
  Postdoctoral or relevant industry experience, analytics team leadership, and scientist mentoring
• Preferred:
  Functional genomics data, CRISPR screen hit-calling frameworks, and library design interpretation
• Preferred:
  Optical pooled CRISPR screening image analysis pipelines
• Preferred:
  Deep learning for image-based phenotypic profiling and cell classification
• Preferred:
  Disease biology and immunology knowledge
• Preferred:
  Network-based analysis frameworks or transfer learning techniques
• Preferred: LLM-powered systems or AI tools for biological data interrogation
• Preferred:
  Interactive data visualization tools such as R Shiny
• Preferred:
  First-author publications or released tools demonstrating novel functional genomics methods

Demonstrates expertise in computational biology and bioinformatics, with a strong focus on developing scalable analytics frameworks and integrating multi-omics data. Proficient in machine learning applications for biological data analysis and experienced in leading teams in functional genomics research.

• Computational Analysis
• Machine Learning Application
• R or Python Proficiency
• Large-Scale NGS Data Analysis
• Functional Genomics Expertise

• Computational Biology
• Bioinformatics
• Algorithm Development
• Statistical Hypothesis Testing
• Single-Cell RNA Sequencing
• Library Design Methods
• Image Analysis Pipelines
• Data Integration
• Deep Learning
• Cell Classification

• Excellent Communication Skills
• Team Leadership
• Mentoring

• CRISPR Screening
• Functional Genomics
• NGS Datasets
• Multi-Omics Data
• The Cancer Genome Atlas
• Dependency Map
• Cancer Cell Line Encyclopedia
• Clinical Proteomic Tumor Analysis Consortium

• AWS Cloud Computing
• Linux Environments
• Version Control (Git)
• R Shiny
• AI Tools