Computational Scientist, Laboratory of Translational Genomics, CGR

Job ID: req3319
Employee Type: exempt full-time
Division: Clinical Research Program
Facility: Rockville: 9615 MedCtrDr
Location: 9615 Medical Center Drive, Rockville, MD 20850 USA

The Frederick National Laboratory is a Federally Funded Research and Development Center (FFRDC) sponsored by the National Cancer Institute (NCI) and operated by Leidos Biomedical Research, Inc. The lab addresses some of the most urgent and intractable problems in the biomedical sciences in cancer and AIDS, drug development and first-in-human clinical trials, applications of nanotechnology in medicine, and rapid response to emerging threats of infectious diseases.

Accountability, Compassion, Collaboration, Dedication, Integrity and Versatility; it's the FNL way.

PROGRAM DESCRIPTION

The Cancer Genomics Research (CGR) laboratory in Gaithersburg, MD, is an advanced, fast-paced, high-throughput organization dedicated to the support of molecular, genetic and epidemiologic studies for investigators at the NCI's Division of Cancer Epidemiology & Genetics (DCEG). The Division includes over 70 principal investigators (PIs) who are at the forefront in cancer epidemiology, genetics, and biostatistics and conduct sophisticated multidisciplinary family- and population-based research utilizing cutting-edge multi-omics technologies to discover the genetic and environmental determinants of cancer. The investigators at DCEG are uniquely positioned to design exemplary studies by collaborating with investigators globally and lead the design and analysis of large-scale genome-wide association studies, studies of tumor characteristics using integrated genomic data analysis and molecular epidemiologic studies based on novel metabolomic and macrobiotic assays. Within DCEG, the Laboratory of Translational Genomics (LTG) staff and investigators conduct studies on germline and somatic genetics of cancer, including analyses of regions of the human genome conclusively identified in cancer-specific genome-wide association studies (GWAS) and family-based studies. The mission of LTG is to understand the contribution of germline and somatic genetic variation to cancer etiology and outcomes and to elucidate underlying molecular mechanisms of these associations. DCEG/LTG is adding computational support, to be provided through CGR, to support methods for large genomic studies including germline analysis, somatic mutation analysis, and integrative tumor analysis. We are seeking a highly motivated Computational Scientist to join the bioinformatics team at the CGR and provide dedicated analytical support for LTG investigators while working in concert with DCEG collaborators, CGR management and staff. The successful incumbent will provide support to the LTG analytical efforts, and will have the opportunity to:

KEY ROLES/RESPONSIBILITIES

• Work closely and learn from expert principal investigators within LTG while supporting a broad portfolio of projects
• Review, QC, and integrate data from multiple sources (multi-omics studies)
• Conduct analysis of genetic and molecular epidemiology data within and across platforms including use of integrative analytic methods
• Organize results into clear presentations and concise summaries of work, in formats useful for scientific interpretation
• Document all analyses and pipelines used in support of reproducible and FAIR research
• Work closely with LTG PIs in support of scientific manuscript development, submission, revision activities with significant co-authorship opportunities
• Participate in mentoring and training next generation of scientists

BASIC QUALIFICATIONS

To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:

• Possession of a (CHEA) Doctoral degree from an accredited college/university in genetics, genomics, bioinformatics, biostatistics, computer science, computational biology or another related field
• Foreign degrees must be evaluated for U.S equivalency
• Effective communication in speech and writing as demonstrated by a track record of publications in peer-reviewed literature as part of a research team.
• The ability to construct practical computational pipelines for data parsing, quality control, modelling and analysis for large-scale genetic or genomics datasets.
• Strong programming skills (e.g., in R, Python)
• Demonstrable shell scripting skills (e.g., bash, awk, sed)
• Experience working in a Linux environment (especially a HPC environment or cloud)
• Ability to obtain and maintain a security clearance

PREFERRED QUALIFICATIONS

Candidates with these desired skills will be given preferential consideration:

• Experience with processing and analyzing large datasets for at least one of the following: GWAS, transcriptomics, methylomics, metabolomics, microbiomics, next-generation sequencing
• Experience in the analysis of data generated by functional genomic methods such as CRISPR and MPRA screens, chromatin interaction and splicing assays
• Proficiency with core statistical and bioinformatics methods (linear regression, logistic regression, eQTL analysis, LDscore regression, credible set and colocalization analysis, etc.)
• Familiarity with public genomic tools, databases, and utilities (UCSC Genome Browser, TCGA, ENCODE, 1000 Genomes, dbGAP, GTEX, SRA NCBI, etc.)
• Experience with various environment/dependency management tools (e.g. pip, venv, conda, renv)
• Knowledge of DevOps tools and technologies, such as Docker/Singularity, GitHub for code management
• Team oriented with demonstrated ability self-educate in current bioinformatics techniques and resources
• Ability to multi-task in a fast-paced environment, organize and execute multiple projects in parallel both independently and as part of working groups

Equal Opportunity Employer (EOE) | Minority/Female/Disabled/Veteran (M/F/D/V) | Drug Free Workplace (DFW)

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