Job Description:

• Design, implement and/or deploy next generation sequencing (NGS) data analysis workflows for data processing, visualization, integration and mining to support novel therapeutic target identification and disease biomarker discovery.
• Data Analysis: apply and, when necessary, identify and implement methods for omics data analysis, and interpretation of genomic data sets (e.g., NGS including single cell and spatial RNA-Seq, proteomic and multi-omics)
• Data Visualization: Fluency in contemporary data visualization methods like R Shiny, D3 Data Integration: Analyzing diverse datasets (multi-omics) to find relevant drug discovery targets and downstream effects relevant to immune disease
• Data Mining: Internal, collaborative, and public databases to assist in the characterization of cardiorenal / liver disease. Select and benchmark methods and tools, define and perform appropriate QC measures. Apply and develop innovative analysis approaches when standard methods are not adequate. Interpret and present analysis results to coworkers and collaborators. Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field. Demonstrates the ability to analyze data from complex experiments, interpret the results, propose appropriate follow-up, and may propose new avenues of investigation. Communicates own work effectively orally and in writing; contributes to writing protocols, procedures, and technical reports. Automate processing and results reporting and delivery. Reports and treats data with a high level of integrity and ethics Complies with applicable regulations; Maintains proper records in accordance with SOPs and policies

Required Skills: 

The ideal candidate will have a strong scientific understanding and experience in bioinformatics in an industrial research environment. The successful candidate will have experience with more than one of the following: analyzing 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 required. 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 and familiarity with code repositories such as GitHub.

Not required, but advantageous: Experience in utilizing LLMs through APIs. Experience with web app development. Experience with statistics and/or machine learning.

Finally, the ideal candidate will have familiarity with computational biology algorithms and tools and experience working with computational biologists to solve problems. Must enjoy working in a multi-disciplinary and collaborative environment. Ability to troubleshoot both individually and as part of a team. Excellent oral and written skills with the ability to communicate in an open, transparent, timely and consistent manner.

Education:

• 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.