Bioinformatics Associate 3

Our client, a world leader in diagnostics
and life sciences, is looking for an "Bioinformatics Associate 3” based out of South San Francisco, CA.

Hybrid Role 2 days onsite

Company Benefits: Medical, Dental,
Vision, Paid Sick leave, 401K

Job Description:

• Pipeline Development: Design, develop, test, and optimize automated bioinformatics pipelines for the processing and analysis of diverse sequencing data.
  
• Data Analysis: Perform advanced statistical and computational analyses of genomics data, including variant calling, differential gene expression, pathway analysis, genomic annotation, and custom analyses tailored to research questions.
  
• Scripting & Programming: Write efficient, well -documented, and reproducible code primarily in Python or R for data cleaning, transformation, integration, and visualization.
  
• Algorithm Implementation: Evaluate, implement, and adapt existing bioinformatics tools and algorithms, as well as contribute to the development of novel methods.
  
• Collaboration: Work collaboratively with wet -lab scientists to understand experimental designs, troubleshoot data quality issues, and interpret biological findings.
  
• Troubleshooting & Optimization: Identify and resolve technical issues within pipelines and analyses, optimize computational efficiency, and ensure data integrity. 
  

• Training in Bioinformatics, Computational Biology, Genomics, Genetics, Computer Science, or a related quantitative field.
  
• Proven strong programming skills in either Python or R is essential. (Please specify which language you are most proficient in and provide examples of your work if possible).
  
• Solid understanding of next -generation sequencing (NGS) data types, experimental design, and common bioinformatics analysis workflows.
  
• Experience with biological databases and public data repositories (e.g., NCBI, Ensembl, UCSC Genome Browser, TCGA, GTEx). Familiarity with version control systems (e.g., Git).
  

• Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Singularity).
  
• Proficiency with workflow management systems (e.g., Snakemake, Nextflow, Cromwell/WDL).
  
• Experience with statistical modeling and machine learning techniques applied to biological data.
  
• Knowledge of specific sequencing applications relevant to our research (e.g., single -cell genomics, long -read sequencing, epigenomics).
  
• Experience working in a Linux/Unix environment. Familiarity with high -performance computing (HPC) clusters. 
  

If interested, please send us your updated
resume at

[email protected]/[email protected]