Senior Engineer, Genomics Data

Redwood City, CA, US
Sep 08, 2019
Science/R&D, Genomics
Required Education
Bachelors Degree
Position Type
Full time
The Genomics Research Center (GRC) is a center of excellence for genetics and genomics that supports both Discovery and Development. The GRC plays an integral role towards our goal of developing world class genetics and genomics research, focusing on finding the right targets and helping us better understand not only human disease biology but also the behavior of and response to our drugs in clinical trials. Within the GRC, the Department of Bioinformatics is responsible for data analysis and provides analytical insight for both internal and external data. This involves the identification and characterization of underlying genetic, epigenetic, or genomic factors that are associated with disease diagnosis, prognosis and response (efficacy and safety) to drug treatment, identification of new targets, and interpretation of the impact of genetic and genomic evidence from population-based studies. We have exciting opportunities for a Senior Engineer in Bioinformatics, based in AbbVie Oncology Hub on the West Coast. The successful candidates will work closely with bioinformaticians across the three sites. Physical location will be in Redwood City

Key Responsibilities:
  • Engineer, maintain, and improve upon existing pipeline code base and enable code reuse across different Linux environments / computing architecture deployed across multiple AbbVie Research sites
  • Use and leverage containers for deploying standard pipelines (Docker, AWS Container, etc.)
  • Run standard pipelines and generating QC reports on one or more of NGS based technologies (RNA-Seq, single cell RNA-Seq, DNA-Seq for Genome/Exome sequencing, ATAC-Seq), Proteomics technologies (Mass Spec based Proteomics), Methylation, Somatic Variant Calling and annotation, Targeted Mutational Profiling, Copy Number Variation, and standard microarray based technologies (Affymetrix, Illumina)
  • Translate and implement algorithms and protocols in a high performance computing environment
  • Develop and maintain data repositories in a structured manner for semi-automated computational reassessment and record keeping
  • Develop web-based applications to enable data query, visualization and custom web-interfaces of data analytics pipelines (e.g. development of R Shiny applications)
  • Understand coding best practices paired with use of tools like Atlassian Suite for feature, request, and documentation tracking.

Level and compensation will be commensurate with experience.

  • BS, MS (preferred), or Ph.D in Computer Science, Bioinformatics, Software Engineering, Computer and Electrical Engineering, or related field, with at least 10+ (BS), 8+ (MS), or 0+ (Ph.D) years of relevant experience.
  • Fluency in 2 or more or relevant programming languages, such as R, Python, Perl, Java, or C++; contemporary data visualization methods like D3, R Shiny, or Spotfire; as well as experience database management in SQL, SQLite, or MongoDB
  • Demonstrated proficiency in a Linux environment and high performance distributed computing.
  • Familiarity with standard tools and data formats related to gene expression, enrichment analysis, genetic, genomic, or epigenetic data, e.g., encountered when analyzing high-throughput transcriptomic, whole exome, whole genome, whole methylome, GWAS, or targeted resequencing data.
  • Strong communication skills in a collaborative environment
Additional desired skills include:
  • Experience analyzing and interpreting omics data for understanding disease mechanisms and model organism prioritization, especially in oncology/immune-oncology
  • Experience handling, visualizing, recording, and managing data with SQL or other enterprise solutions
  • Familiarity with annotation complexities (gene and features) from the various public annotation sources and how to map vendor assay technologies to current annotation based on their intended and unintended design.
  • Fluency with consortium disease specific (like the Cancer Genome Atlas, GEO, etc) or genomic databases (such as those relating to genome annotation, genetic variants, public data repositories).
  • Ability to perform or assist with high dimensional data mining, data integration and data extraction projects of internal and/or external public data via development and/or application of machine learning or artificial intelligence methods. Trained expertise in machine learning or artificial intelligence is highly desirable.
  • Attention to details, strong organizational, written and oral communication skills in a collaborative environment
  • Enjoy working in a fast paced and highly collaborative dynamic environment with a diverse team to tackle complex problems.
  • Experience interpreting biological data related to diseases associated with oncology/immune-oncology