Metagenome Research Scientist (ID: 107907)

What You Will Do

• Lead large-scale analyses of longitudinal metagenomic datasets (e.g., NEON and MONet) to investigate microbial community assembly, succession, stability, and interactions across environmental gradients.
• Develop scientific directions, lead programmatic initiatives, support JGI users, and contribute to Metagenome Program production activities.
• Formulate and test ecological hypotheses and develop innovative, theory-driven analytical frameworks to understand spatiotemporal microbial interactions.
• Design, benchmark, and implement scalable computational methods and workflows for analyzing longitudinal metagenomic and metatranscriptomic data.
• Collaborate with JGI data scientists and computational teams to leverage the Data Lakehouse, integrating time-series data, MAG abundance profiles, and environmental metadata into predictive models of microbial function.
• Provide scientific leadership for CSP and FICUS projects, collaborating with JGI users, DOE BER investigators, NEON, EMSL, and external partners.
• Partner with JGI production, project management, IMG, and GOLD teams to operationalize research methods into standardized, reusable tools for the broader user community.
• Lead scientific publications and present findings at conferences, consortium meetings, and JGI user meetings.
• Supervise and develop team members, including recruitment, work direction, performance management, and professional development.
• Mentor and train postdoctoral researchers and technical staff in longitudinal metagenomics, ecological statistics, and data synthesis.
• Represent the JGI in cross-institutional working groups and workshops focused on continental-scale ecology, time-series standards, and environmental microbiome dynamics.

What We Are Looking For

• A Ph.D. (or equivalent knowledge/training) in Bioinformatics, Computational Biology, Microbiology, Molecular Biology, Biochemistry or a related discipline and a minimum of 3 years of demonstrated expertise in Microbial Ecology or an equivalent combination of education and experience.
• Demonstrated experience analyzing complex metagenome, metatranscriptome, and genomic datasets, and longitudinal and time-series data analysis, assembly, binning and metagenome-assembled genome (MAG) generation, functional annotation, and taxonomic profiling.
• Demonstrated expertise in Microbial Ecology, including community assembly rules, niche differentiation, metabolic handoffs, spatiotemporal dynamics, and interaction network topology.
• Experience working in high-performance computing (HPC) environments to analyze large-scale biological datasets.
• Demonstrated proficiency with statistical programming and computational tools, such as Python, R, and UNIX/Linux environments, for advanced multivariate data analysis, time-series modeling, and visualization.
• Demonstrated ability to develop and scale reproducible computational workflows (e.g., WDL, Nextflow, and Snakemake) for large, multi-sample datasets.
• A strong record of impactful, peer-reviewed scientific publications demonstrating contributions to ecological hypothesis testing, microbiome analysis, or multi-omic data integration.
• Strong analytical skills, including the ability to develop creative solutions to complex problems, while addressing research needs in an effective and timely manner.
• Excellent organizational skills, including the ability to manage complex, multi-tiered projects with competing priorities, while meeting milestones.
• Demonstrated oral, written, and presentation skills including the ability to communicate complex ecological and computational concepts effectively with internal and external staff, JGI users, and scientific collaborators.
• Excellent interpersonal communication skills including collaborating effectively across interdisciplinary teams, bridging ecological domain science with computational engineers, data scientists, and external biological research.

Desired Qualifications

• A Ph.D. in Bioinformatics, Computational Biology, Microbiology, Molecular Biology, Biochemistry or a related discipline.
• Familiarity with continental-scale environmental monitoring programs (e.g., NEON, MONet) or longitudinal soil and aquatic microbiome datasets.
• Experience applying machine learning, network inference, dynamic systems modeling, or predictive modeling to investigate microbial interactions.
• Demonstrated ability to effectively supervise staff, including assigning work, providing coaching and performance feedback, and supporting employee development.