Strong scientific understanding and experience in bioinformatics 

 Experience in pharmaceutical research a plus 

 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

  Data Analysis : Implement methods for omics data analysis, and interpretation of genomic data sets (e.g., bulk RNA-seq, scRNA-seq, ATAC-Seq) 

  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 immune 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 interpret the outcome of experiments, 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 

The successful candidate will have 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 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 

 Familiarity with computational biology 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 

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.