Bioinformatics Analyst (Multiple Levels)

The Cancer Genomics Research Laboratory (CGR) investigates the contribution of germline and somatic genetic variation to cancer susceptibility and outcomes in support of the National Cancer Institute (NCI)'s Division of Cancer Epidemiology and Genetics (DCEG). Working in concert with epidemiologists, biostatisticians and basic research scientists in DCEG's intramural research program, CGR provides the capacity to conduct genome-wide discovery studies and targeted regional approaches to identify the heritable and acquired determinants of various forms of cancer. This includes the design and analysis of high throughput studies using various types of “-omics” technologies such as sequence- and array- based genome-wide association studies, and generation of comprehensive data on histological and molecular profiling of tumors and their precursors to identify environmental and germline risk factors for cancer through integrative analytics. We are seeking promising candidates to join the bioinformatics team at the CGR to work with DCEG investigators, external collaborators, CGR management and staff to provide support to the extensive DCEG research portfolio. This is a general position description, and we will review promising candidates for potential recruitment at multiple levels depending on education and relevant experience.

KEY ROLES/RESPONSIBILITIES

This position description does not represent a current opening but may be used to identify candidates with skills and experience for positions within Leidos Biomedical Research that frequently become available. Candidates who express an interest may be considered for future positions at Leidos Biomedical Research.

• Building, benchmarking, administrating, and maintaining bioinformatics pipelines to facilitate high-throughput genomic data analysis in an HPC environment
• Performing large-scale data analysis and other support using third party software and/or CGR's in-house pipelines for sequencing data analysis, including (but not limited to) data QC, reference mapping, variant calling, variant filtering and classification, annotation, and statistical analyses
• Performing large-scale data analysis and other support using third party software and/or CGR's in-house pipelines for genotyping data analysis, including (but not limited to) data QC, phasing and imputation, population structure analysis, association analysis, meta-analysis, and fine mapping
• Strong experience in exploring and identifying novel software solutions, establishing and benchmarking against reference datasets, evaluating and troubleshooting new or established pipelines
• Implementing relational database models and web portals for scientific data
• Conduct programming and/or scripting using languages such as Python, Perl, Bash, R and/or Java
• Accessing, extracting and preparing data for analysis, including combining data run on multiple platforms as well as externally generated data in support of meta-analyses
• Development and execution of advanced analyses including multiplicative interaction analyses, pathway-based analyses, integrative analyses from multiple platforms and various data types
• Organizing results into clear presentations and concise summaries of work, in formats useful for scientific interpretation

BASIC QUALIFICATIONS

• Possession of an BA/BS degree from an accredited college/university according to the Council for Higher Education Accreditation (CHEA) in bioinformatics, statistics, genetics, computational biology or related field (Additional qualifying experience may be substituted for the required education). Foreign degrees must be evaluated for U.S. equivalency
• In addition to basic educational requirements, advanced degrees (MS, PhD) and work experience may be required and will be assessed in position placement
• Demonstrated experience in processing and analyzing sequencing, genotyping, expression and other data utilizing bioinformatics tools is essential
• Demonstrated experience in constructing practical computational tools for data parsing, quality control, modelling and analysis for large-scale genetic or genomics datasets
• Strong knowledge required of genetic association analysis and interpretation, with applied computational research on large multivariate datasets
• Expertise in algorithmic implementation, statistical programming and data manipulation, using e.g. R/Bioconductor, Matlab, Python, and a wide range of contemporary, open-source bioinformatics tools and database structures
• Strong programming experience with Shell, Python, Perl, C/C++, and/or JAVA
• Experience with design and use of relational SQL databases
• Experience with molecular and population genetics

PREFERRED QUALIFICATIONS

• Familiarity with publicly available data sources (such as dbGaP, GDC/TCGA, ENCODE, 1000 Genomes, TARGET, GTEX) and diverse genomic annotations
• Experience managing large datasets and computational tasks, experience working in a Linux environment (especially a compute cluster environment)
• Experience with AWS EC2/ELB/S3 or other cloud computing environments
• Experience with Snakemaker or other workflow languages
• Experience with Singularity or other container-based platforms
• Experience with bulk, targeted, and/or single cell RNA sequencing analysis
• Experience with methylation analysis, primarily targeted (EPIC Illumina array)
• Ability to carry out a wide range of bioinformatics techniques and analyses, and benchmark/troubleshoot with minimal supervision
• Careful attention to detail. Must be analytical, flexible, innovative, and self-motivated
• Ability to organize and execute multiple projects in parallel
• Demonstrated ability to self-educate in current bioinformatics techniques and resources
• Excellent written and oral communication-ability to communicate clearly and effectively with staff and other groups within CGR and DCEG, and able to interpret data and write scientific reports as well as other written communications