Bioinformatic Scientist

About the position We have an opening for a Bioinformatic Scientist to conduct research training and evaluating next-generation bioinformatic and data management tools. As a member of our multidisciplinary team, you will collaborate with experts in artificial intelligence, machine learning, computational biology, software engineering, and interface with experimentalists generating large datasets. You will explore opportunities to further scientific capabilities in detection and surveillance of novel and emerging pathogens. You will develop new methods, tools, and algorithms for metagenomic sequence data analysis and pathogen characterization. You will work with multiomic datasets and apply state of the art machine learning models for integrated data analysis and prediction. This position is in the Genomics group in the Biosciences and Biotechnology Division. This position may offer a hybrid schedule, which includes the flexibility to work from home one or more days per week, after a probationary period. The specifics of the hybrid schedule, including the exact number of days required in the office and virtual work options may vary based on the needs of the team and the organization.

Responsibilities

• Analyze metagenomic datasets from environmental (e.g., wastewater, aerosol) and clinical (nasal swab, saliva, etc) samples to determine taxonomy, function and predict impact.
• Research, develop, and implement computational and statistical techniques for analyzing novel and unknown sequences.
• Conduct multiomic data analysis including genomics, transcriptomics, proteomics and metabolomics for biomarker discovery.
• Contribute to biological data management such as metadata schemas, databases, data catalogs and integrated workflows.
• Contribute to and influence the development of innovative projects, principles, and ideas in biosurveillance, biodetection and microbiome.
• Routinely interact with technical contacts at sponsor and partner organizations; represent the organization on specific technical projects.
• Determine, propose, and implement advanced analysis methodologies and contribute to identifying future research directions and proposals that will secure future projects in the field.
• Contribute to the completion of project milestones, balance multiple projects/tasks and priorities to ensure deadlines are met, working independently with minimal direction within the scope of assignments.
• Lead the development of manuscripts and presentations documenting project activities and research results.
• Oversee the activities of other personnel, providing informal mentoring and guidance to less-experienced team members.
• Perform other duties as assigned.

Requirements

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• PhD in Bioinformatics, Computational Biology, Computational Bioengineering, Machine Learning, Statistics, Computer Science, Mathematics, or a related field.
• Comprehensive knowledge and experience developing and applying methods in one or more of the following bioinformatic areas: metagenomics, transcriptomics, metabolomics, proteomics, taxonomy and function analysis, multimodal data integration.
• Experience in large data management such as metadata schema development, data cataloging, database development, and integration with pipelines, AI/ML models and workflows.
• Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or running numerical simulations of complex workflows.
• Advanced verbal and written communication skills necessary to collaborate within a team environment and present technical information to varied audiences.
• Effective interpersonal skills and initiative necessary to interact with all levels of personnel and work independently in a collaborative, multidisciplinary team environment.
• Demonstrated ability to balance multiple projects and prioritize competing demands while maintaining high-quality standards for deliverables.
• Demonstrated ability to provide guidance and informal mentoring to other personnel and junior team members.
• Demonstrated ability to represent the organization as a primary technical contact and to contribute to the development of innovative projects, principles, and ideas.

Nice-to-haves

• Domain knowledge in protein and genome language models sufficient to communicate effectively with team members and subject matter experts.
• Experience and advanced knowledge in developing and applying algorithms in advanced machine learning areas.
• Experience developing and implementing machine learning models such as deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning.

Benefits

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (depending on project needs)

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