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# Associate Director, Data Science, Functional Genomics

- **Company:** Merck Sharp & Dohme LLC
- **Location:** South San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $176,200.0 - $277,300.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Amazon Web Services, Business Analytics Applications, Data Analysis, Bioinformatics, Profiling, Computational Biology, Computer Simulation, Image Analysis, Directed Acyclic Graph (Directed Graphs), Information Engineering, Data Infrastructure, Data Stores, Data Visualization, R (Programming Language), Statistical Hypothesis Testing, Python (Programming Language), Linux System Administration, Machine Learning, Software Engineering, Systems Integration, Visual Analytics, Cloud Platform System, Transfer Learning, Large Language Models, Deep Learning, Git, Information Technology, Software Version Control
- **Published:** September 3, 2026
- **Apply:** https://www.nexxt.com/job.asp?id=3375525971&tx=JP4741FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* MS in computational biology, bioinformatics, biostatistics, biophysics, mathematics, statistics, genetics/genomics, computer science or a related STEM discipline and a minimum of 8 years of relevant professional experience, including hands on experience analyzing large scale NGS and functional genomics datasets.
 * 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., * A Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Statistics, Computer Science, Mathematics, Biophysics, Genetics/Genomics, or a related STEM field with 4+ years of professional experience.
 * 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 multiomics readouts.
 * A track record of developing novel functional genomics methods, demonstrated through first author publications or released tools.

Travel

Up to 10% travel is required., Amazon Web Services (AWS), Bioinformatics, Biological Data Analysis, Computational Biology, Computational Genomics, CRISPR-Cas System, Data Engineering, Data Modeling, Data Science, Data Visualization, Gene Expression Profiling, Genome, Genome Sequencing, Genomic Analysis, Genomics, Machine Learning (ML), Omics, RNA-Seq, Single-Cell Genomics, Software Development

## 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 for our Translational Genome Analytics (TGA) team. In this role, you will lead our Functional Genomics & Predictive Modeling 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 technical and scientific leader who shapes early discovery direction, mentors 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.
 * Lead and mentor a team of scientists, set the 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.


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