Senior Scientist, Computational Biology (Industry)

South San Francisco, CA 94080
United States
Job Posted Date: 
July 30, 2020
Full-time Positions
Life Sciences

Merck & Co., Inc. Kenilworth, N.J., U.S.A. known as Merck in the United States and Canada, is a global health care leader with a diversified portfolio of prescription medicines, vaccines and animal health products. The difference between potential and achievement lies in the spark that fuels innovation and inventiveness; this is the space where Merck has codified its legacy for over a century. Merck’s success is backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.

Merck is on a quest for cures and is committed to being the world’s premier, most research-intensive biopharmaceutical company. Today, we’re doubling down on this goal. Merck Research Laboratories is a true scientific research facility of tomorrow, and will take Merck’s leading discovery capabilities and world-class small molecule and biologics R&D expertise to create breakthrough science that radically changes the way we approach serious diseases. 

The Genetics and Pharmacogenomics (GpGx) department is looking for a passionate and talented computational biology scientist to join our Oncology Preclinical Analytics research team based in South San Francisco, CA. In this role, you will collaborate with cross-functional teams of computational biologists along with colleagues in Discovery Research to drive preclinical target discovery and drug development efforts through application of bioinformatics expertise, analysis and interpretation of biological findings. Oncology research at Merck is driven by a deep interest in the biology of tumor microenvironment and how diverse points of intervention can be combined to achieve ever higher rates of durable response and patient overall survival.

In this exciting role, you will:

  • Contribute to multiple stages of drug discovery by interrogating high-throughput assays, including genomics, transcriptomics and proteomics datasets. Collaborate with experimental scientists to characterize novel targets coming from genetics, translational and disease pathway exploration, explore target engagement, research mechanisms of action, and provide functional validation of novel drug targets.
  • Work with large internal and public biological data sets including Next Generation Sequencing (NGS) data (e.g. RNA-Seq, single cell RNA-Seq, WGS, CRISPR)
  • Be proactive and work collaboratively across disciplines, including molecular biologists, bioinformaticians, and software engineers
  • Employ best reproducible research and data integrity practices to generate reusable analysis frameworks and reports to support Discovery Oncology target identification and validation efforts.
Job Requirements: 

Education Minimum Requirement: 

Ph.D. in Bioinformatics, Biostatistics, Computational biology, Computer Science, Genetics, Immunology, Mathematics, Molecular Biology, Statistics or related field -or-

Masters’ degree in the above disciplines, with 3 years of relevant experience


Required Experience and Skills: 

Passion to solve biological problems and identify problems that can be efficiently solved through computational methods and algorithms

  • Experience with computational analysis and interpretation of large-scale NGS datasets
  • Proficiency in at least one statistical programming language, such as R or Python
  • Familiarity with public databases and repositories of DNA and RNA profiling data
  • Demonstrate the ability to learn, be proactive and motivated, and consistently focus on details and execution
  • Excellent oral and written communication skills

Preferred Experience and Skills:

  • Previous experience with experimental design, statistical hypothesis testing, and biological interpretation
  • Skilled at integrating results generated from multiple data sources and assay types to strengthen research hypotheses
  • Understanding the pros and cons of various algorithms for DNA-seq, RNA-seq and single-cell RNA-seq analysis
  • Experience with version control environments, such as Git
  • Experience with high-performance Linux cluster and cloud computing
  • Familiarity with algorithms employed in DNA-seq, RNA-seq, single-cell RNA-seq or CyTOF analysis is a plus


  • Up to 10% travel is required
Greater Bay Area