Bioinformatics Manager, CGR

Job ID: req2569
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

We are seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and help direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

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), the world's most comprehensive cancer epidemiology research group. CGR is located at the NCI-Shady Grove campus in Gaithersburg, MD and is operated by Leidos Biomedical Research, Inc. We care deeply about discovering the genetic and environmental determinants of cancer, and new approaches to cancer prevention, through our contributions to the molecular, genetic, and epidemiologic research of the 70+ investigators in DCEG. Our bioinformaticians have both the passion to learn and the opportunity to apply their skills to our rich and varied datasets, generated in support of DCEG's multidisciplinary family- and population-based studies. Working in concert with the epidemiologists, biostatisticians, and basic research scientists in DCEG's intramural research program, CGR conducts genome wide association studies (GWAS), targeted, whole-exome, and whole-genome sequencing studies, including analysis of germline and somatic variants, transcriptomics, epigenomics, structural variation, copy number variation, metagenomics, viral genomics, and more. The successful candidate will have responsibility for the day-to-day management of bioinformatics activities and will be tasked with collaborating with our DCEG scientists to deliver high quality, well annotated and interpretable datasets. He/she must demonstrate scientific and technical leadership for the group and horizon scanning for future approaches, as well as establishing, maintaining, and monitoring the key performance indicators for the group. If you have experience managing a diverse group of interdisciplinary scientists, then come lead our talented team of bioinformaticians dedicated to understanding the genetics of cancer!

KEY ROLES/RESPONSIBILITIES

• Function as a scientific thought leader within the CGR bioinformatics group and the CGR leadership team, and manage collaborative analytical efforts with DCEG investigators with an eye towards scientific productivity and reproducibility
• Provide technical expertise within a highly collaborative environment and support and establish a culture of engagement, innovation, and continuous improvement
• Collaborate closely with DCEG PIs on scientific manuscript development, submission, and revision activities with significant co-authorship and potentially first authorship opportunities
• Utilize computational biology methods to integrate, analyze and visualize results with data sets obtained from a wide variety of genotyping platforms including whole-genome, methylation and targeted genotyping data generated by multiple applications, including Illumina Infinium, Illumina EPIC, TaqMan, qPCR and others
• Utilize computational biology methods to integrate, analyze and visualize results with data sets obtained from a wide variety of sequencing platforms including whole-genome, exome, transcriptome, methylome, microbiome, and targeted sequencing data generated by multiple technologies, including Illumina, Pacific Biosciences, Ion Torrent, Oxford Nanopore and others
• Manage and coordinate with additional resources throughout DCEG, including fellows, post-docs, other contract resources to support an integrative, collaborative, collegial environment

BASIC QUALIFICATIONS

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

• Possession of a Bachelors' degree from an accredited college/university according to the Council for Higher Education Accreditation (CHEA) in computer science, software engineering, bioinformatics, statistics, or related field required. Foreign degrees must be evaluated for U.S equivalency
• In addition to educational requirements, a minimum of four (4) years progressively responsible scientific software engineering and/or complex system management/bioinformatics experience, including 2+ years of experience supervising or leading teams or projects
• Team-oriented with excellent written and verbal communication skills, organizational skills, and attention to detail; ability to organize and execute multiple projects in parallel both independently and as part of working groups, interact effectively with cross-functional teams, including laboratory, LIMS, project management, data scientists, epidemiologists and biostatisticians
• Demonstrated experience in successfully managing bioinformaticians and serving as a mentor for other bioinformatics and/or software developer staff members on analysis approaches and best practices
• Proven ability and demonstrated strategies to stay current with new bioinformatics analysis tools and software technologies
• Experience with a variety of programming languages and environments such as C/C++, Java, or Rust, and at least one scripting language such as Bash, Python, Perl, or R

PREFERRED QUALIFICATIONS

Candidates with these desired skills will be given preferential consideration:

• Masters or PhD strongly preferred
• Experience managing large computational tasks in a Linux-based high-performance computing environment
• Experience managing complex pipelines, including support of best-practice full lifecycle software development experience and source code management (Git, SVN, unit testing) is required
• Proven experience and ability to benchmark tools and compare performance to existing pipelines
• Familiarity with workflow management systems and environment/dependency management tools
• Familiarity with diverse primary sequencing tools (e.g. BWA/Novoalign, tophat/star/bowtie, bedtools, GATK suite, SV/CNV detection, somatic/low AF mutation detection, IGV, Samtools), Long reads (e.g. PacBio, Oxford Nanopore) based analysis (repeat analysis, assembly, large mutation detection, phasing), Microbiome analysis tools (e.g. QIIME II, DADA2, MetaPhlAn), GWAS QC and analysis tools (e.g. KING, GRAF, EAGLE, minimac, plink, Michigan Imputation Server, SNPtest, SAIGE, BOLT-LMM, VCFtools), and annotation tools (e.g. snpEff, Annovar, SIFT, PolyPhen)
• Experience with Google Cloud, AWS, or managed cloud environments
• Experience with CI/CD (GitLab CI/CD, GitHub Actions, Travis CI, CircleCI, etc.)
• Experience in using project management approaches for documentation and communication (Fogbugz, JIRA, Teams, Slack) to document/summarize project status and development activities
• Knowledge of various DevOps tools and technologies, such as Docker/Singularity, Kubernetes, Ansible/Terraform
• Familiarity with relational and NoSQL databases design, data warehousing, and data modeling (e.g. MySQL, Amazon Redshift, MongoDB, Elasticsearch, FileMaker)
• Hands-on experience with commercial and open-source bioinformatics and genomics analysis software (IVA, GoldenHelix, Sentieon)
• Ability to incorporate multiple data sources for use in downstream analysis, including microarray genotype data, epidemiological data, publicly available data sources (e.g. TCGA, ENCODE, 1000 Genomes, COSMIC, TopMed, gnomAD, ESP) and diverse genomic annotations
• Experience with prioritization in a high-pressure environment

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

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