Bioinformatics Analyst III – Cancer Genomics Research (NCI)

Gaithersburg, Maryland / Montgomery Village, Maryland
Sep 19, 2017
Required Education
Bachelors Degree
Position Type
Full time
The Cancer Genomics Research Laboratory (CGR) investigates the contribution of germline and somatic genetic variation to cancer susceptibility and outcomes in support of the 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 determinants of various forms of cancer.
The Cancer Genomics Research (CGR) laboratory in Gaithersburg, MD, is a fast-paced, high-throughput organization dedicated to the support of molecular, genetic and epidemiologic studies for investigators at the National Cancer Institute's Division of Cancer Epidemiology & Genetics (DCEG). The Division includes over 70 principal investigators in epidemiology, genetics, and biostatistics who conduct multidisciplinary family- and population-based research to discover the genetic and environmental determinants of cancer, and new approaches to cancer prevention. This includes the design and analysis of high throughput studies using various types of “–omics” technologies such as array- and sequence-based genome-wide association studies, studies of tumor characteristics using integrated genomic data analysis and molecular epidemiologic studies based on novel metabolomic and microbiomic assays.
We are seeking a highly motivated scientist to join the bioinformatics team at the CGR and provide analytical support to DCEG. Working with DCEG investigators, external collaborators, CGR management and staff, the successful incumbent will provide leadership and support to the extensive DCEG analytical efforts, specifically:
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
GWAS analytical support including data QC, imputation, population structure analysis, association analyses
Sequencing analytical support including data QC, reference mapping, variant calling, annotation and filtering.
Organizing results into clear presentations and concise summaries of work, in formats useful for scientific interpretation
Development and execution of advanced analyses including multiplicative interaction analyses, pathway based analyses, integrative analyses from multiple platforms and various data types.
Developing and maintaining bioinformatics pipelines.
To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:
Possession of a Bachelor’s degree (Master’s Degree preferred) from an accredited college or university according to the Council for Higher Education Accreditation (CHEA) in bioinformatics, statistics, genetics, computational biology or related field
Foreign degrees must be evaluated for U.S. equivalency
A minimum of five (5) years of progressively responsible scientific and/or complex system management/bioinformatics experience.
Hands-on experience in processing and analyzing sequencing, genotyping, expression and other data utilizing bioinformatics tools is essential.
Strong knowledge required of genetic association analysis and interpretation, and 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 (e.g. PLINK, SNPTEST, GLU, IMPUTE2, BEAGLE, UCSC Genome Browser, etc.)
Programming experience with Shell, Perl, Python, C/C++, and/or JAVA
Team oriented with excellent written and verbal communication skills, with demonstrated ability to self-educate in current bioinformatics techniques and resources
Must be able to obtain and maintain a security clearance
Candidates with these desired skills will be given preferential consideration:
Ph.D. in bioinformatics, computer science, computational biology, or related field
Familiarity with publicly available data sources (such as dbGaP, 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 in the field of molecular and population genetics with a strong publication record