Scientist III

About the role

Design, implement, and/or deploy NGS data analysis workflows for data processing, visualization, integration, and mining to support novel therapeutic target identification and disease biomarker discovery.

Responsibilities

• Implement methods for omics data analysis and interpretation of genomic data sets (e.g., bulk RNA-seq, scRNA-seq, ATAC-Seq).
• Develop fluency in contemporary data visualization methods like R Shiny and D3.
• Analyze diverse datasets (multi-omics) to identify relevant drug discovery targets and downstream effects relevant to immune disease.
• Mine internal, collaborative, and public databases to assist in the characterization of immune disease.
• Select and benchmark methods and tools, define and perform appropriate QC measures.
• Apply and develop innovative analysis approaches when standard methods are inadequate.
• Interpret and present analysis results to coworkers and collaborators.
• Follow relevant scientific literature to ensure the use of optimal methods and understand emerging practices.
• Interpret experimental outcomes, propose follow-up investigations, and suggest new research avenues.
• Communicate work effectively orally and in writing; contribute to protocols, procedures, and technical reports.
• Automate processing, results reporting, and delivery.
• Report and treat data with high integrity and ethics.
• Comply with applicable regulations; maintain proper records in accordance with SOPs and policies.

Requirements

• Experience with more than one of the following: analyzing next-generation sequencing (NGS), functional genomics, statistics for big data analysis, or multi-omics data integration.
• Good knowledge of existing bioinformatics databases and file formats.
• In-depth understanding of computational methods for NGS analysis and the usage of public data resources.
• Strong hands-on skills in relevant programming languages (e.g., R, Python, Shiny, UNIX/Linux, UNIX bash shell scripting, Nextflow), statistical software, cloud computing, visualization tools, and relevant R/Bioconductor packages.
• Demonstrated ability to produce well-designed and documented code.
• Familiarity with computational biology tools and experience working with computational biologists to solve problems.
• Ability to troubleshoot both individually and as part of a team.
• Excellent oral and written communication skills with the ability to communicate openly, transparently, and consistently.

Qualifications

• Master’s degree from an accredited institution with one-plus (1+) years of experience in a related scientific discipline (Computer Science, Genomics, Biostatistics, or Bioinformatics preferred).
• OR Bachelor’s degree from an accredited institution with seven-plus (7+) years of experience in a STEM discipline.