Skip to main content
Discovering the causes of cancer and the means of prevention
 

Staff Scientist, Occupational and Environmental Epidemiology Branch

The Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH), is recruiting a computational analyst/bioinformatician for the analysis of large-scale genetic association studies in the Occupational and Environmental Epidemiology Branch (OEEB).

OEEB integrates genetic, molecular and epidemiologic data to understand cancer etiology, chemical carcinogenesis and mechanisms of action of known or suspected occupational and environmental carcinogens. Branch investigators are leading large consortia studies that evaluate the contribution of germline genetic variation to cancer susceptibility with related projects integrating transcriptomic, epigenetic, and other high-dimensional biomarker data and somatic variation to gain insight into functional mechanisms.

The successful candidate will provide bioinformatic analytical support for multiple projects as a staff scientist to OEEB investigators, including the following activities: perform large-scale genotyping quality control, phasing and imputation, population structure assessment, variant association testing, LD-score regression, trans-ancestry meta-analyses, rare variant burden testing, fine-mapping, pathway and other related analyses for genome-wide association studies (GWAS); develop and maintain necessary bioinformatics pipelines for conducting state-of-the art genetic analyses; utilize open-source programs (e.g., SAIGE, SKAT) for analysis of GWAS and next generation sequencing (NGS) data; process and utilize publicly available datasets (e.g., UK Biobank, gnomAD, GTEx) for analysis; organize results into clear presentations and concise summaries of work; and grow and maintain experience with state-of-the-art bioinformatics tools and data repositories.

The successful candidate must hold a doctoral degree in bioinformatics, biostatistics, computer science, computational biology or other related disciplines. The candidate must have experience processing and analyzing large genetic datasets (particularly for GWAS) and possess expertise in algorithmic implementation, statistical programming and data manipulation and proficiency in programming using Python, Perl, R, C/C++, and/or JAVA. The successful applicant should possess many of the following skills: experience with open-source bioinformatic tools for genetic analyses (e.g., GCTA, SAIGE, BOLT-LMM, FINEMAP, PrediXcan, LDSC, RAREMETAL, MAGMA, DEPICT); proficiency in R/Bioconductor; experience with public bioinformatic/genetic databases (e.g., dbGAP, TCGA, ENCODE, gnomAD, 1000 Genomes, and GTEx); experience with bash scripting and working in a Linux environment (especially a computer cluster environment); experience with core statistical methods (e.g. linear regression, logistic regression, linear mixed models, etc.); and a demonstrated ability to self-educate in current and evolving bioinformatics techniques and resources. The ability to communicate effectively in speech and in writing is important, as demonstrated by a track record of publications in peer-reviewed literature as part of a research team or collaborative projects.

Applications will be accepted through December 13, 2020. Selection for this position will be based solely on merit, with no discrimination for non-merit reasons such as race, color, religion, gender, sexual orientation, national origin, political affiliation, marital status, disability, age, or membership or non-membership in an employee organization. NIH encourages the application and nomination of qualified women, minorities and individuals with disabilities. NIH provides reasonable accommodations to applicants with disabilities. If you require reasonable accommodation during any part of the application and hiring process, please notify us. The decision on granting reasonable accommodation will be made on a case-by-case basis. This position is subject to a background investigation. Salary is commensurate with research experience.

Interested individuals should send a cover letter, curriculum vitae, brief summary of research interests and experience, and two letters of reference to:

Ms. Cynthia Drice
Division of Cancer Epidemiology and Genetics, National Cancer Institute
9609 Medical Center Drive, Rm. 7E336 MSC 9775
Rockville, MD 20850 / E-mail: cynthia.drice@nih.gov

The closing date for applications is December 13, 2020.

DHHS, NIH, and NCI are Equal Opportunity Employers