Associate Scientist II, Genomics Data

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas - immunology, oncology, neuroscience, and eye care - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on Twitter, Facebook, Instagram, YouTube and LinkedIn.

Job Description

Key Responsibilities

• Engineer, maintain, & improve upon pipeline code base for bioinformatics processing & analysis of various genomics data types.

• Enable code reuse across different Linux environments / computing architecture deployed across research sites.

• Facilitate internal & external data availability to bioinformaticians.

• Develop & maintain structured data repositories for semi-automated computational reassessment & record keeping.

• Develop & support R Shiny applications to enable data query, visualization & custom web-interfaces of data analytics processes & pipelines.

• Interface with research-serving IT specialists to assure availability of appropriate HPC resources (compute, storage, networking).

• Leverage bioinformatics & genomics data knowledge to gather requirements from stakeholders & collect feedback for continued development & support.

• Engage in effective communications with stakeholders & other team members via asynchronous collaboration tools (e.g. Microsoft Teams).

• Utilize R & Python, including experience with web-app frameworks such as R Shiny & Flask.

• Build workflows & pipelines to support Illumina NGS data generation, processing, & analysis (particularly Bulk RNAseq and scRNA-seq).

• Structure data storage technologies including relational &/or NoSQL.

• Generate ML models for data classification & analysis such as KNN, Random Forest, &/or SVN.

• Use version control systems such as Git or GitHub, & software project management systems such as Jira.

• Wrangle, manage, & leverage large datasets such as patient cohorts & public data repositories.

• Utilize contemporary interactive data visualization methods such as R Shiny, D 3, or Spotfire.

• Use pipeline building tools including CWL &/or Snakemake.

• Utilize tools & data formats related to gene expression, enrichment analysis, genetic, genomic, or epigenetic data such as encountered when analyzing high-throughput transcriptomic, whole exome, whole genome, whole methylome, GWAS, or targeted resequencing data.

Qualifications

Must possess a Master's degree or foreign academic equivalent in Biology, Chemistry, Biochemistry, Computer Science, Bioinformatics, Software Engineering, Computer & Electrical Engineering or a highly related field of study with an academic or industrial background in:

• developing & supporting R Shiny applications to enable data query, visualization & custom web-interfaces of data analytics processes & pipelines;
• utilizing R & Python, including experience with web-app frameworks such as R Shiny &/or Flask;
• structuring data storage technologies including relational &/or NoSQL; &
• using version control systems such as Git &/or GitHub, & software project management systems such as Jira.