Associate Director, Functional Genomics, Data Science

• 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.

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

Demonstrates expertise in computational analytics frameworks, integrating functional genomics with high throughput datasets, and applying machine learning techniques to biological data analysis. Proven ability to lead teams, mentor scientists, and drive standards for reproducible research.

• Ph.D. In Bioinformatics
• Machine Learning Application
• Statistical Programming (R, Python)
• Experience With NGS Data Analysis
• AWS Cloud Computing Infrastructure

• Computational Analysis
• Algorithm Development
• Biological Interpretation
• Statistical Hypothesis Testing
• Single Cell RNA Sequencing Analysis
• Experimental Design
• Data Integration
• Quality Control Pipelines
• Longitudinal Readout Analysis
• Morphological Feature Extraction

• Excellent Communication Skills
• Proactive Focus on Details
• Mentoring

• Functional Genomics
• Transcriptomics
• Proteomics
• Drug Discovery
• Therapeutic Area Target Identification

• Version Control (Git)
• Linux Environments
• Public Data Repositories
• FAIR Data Infrastructure
• AI and ML Approaches