Lab Research Analyst I

David Lab Job Description

The David Lab at Duke University (www.ladlab.org) is recruiting a Laboratory Research Analyst I to join a team of scientists developing and using new DNA sequencing tools to study diet. We are particularly interested in creating new ways to measure and analyze food intake, which is essential for monitoring and improving human health. We are developing new DNA sequencing-based techniques that track the dietary plant and animal species people consume. Our techniques generate objective data on intakes of over 900 plant and animal species, robust to differences in memory/recall, and standardized across languages and cultures using the universal language of DNA. We collaborate with researchers and communities across North Carolina and the world. Our studies will also create new models of disease risk and examine geographic, cultural, behavioral, and socioeconomic determinants of diet. Our projects are funded by awards from Schmidt Sciences, the Chan Zuckerberg Initiative, the National Institutes of Health, the Gerber Foundation, and the Burroughs Wellcome Fund.

We seek a Laboratory Research Analyst I to own the research-data operations behind our DNA sequencing studies, connecting the lab's molecular and sequencing work to the datasets and systems the whole team relies on. Working independently and in regular consultation with the Principal Investigator, the analyst will run and help develop our FoodSeq bioinformatics pipelines, perform quality control on sequencing data, and manage sample and sequencing-run metadata across multiple cohorts, from sample receipt through analysis-ready output. Because this work sits at the interface of our wet-lab, field-sampling, and computational efforts, it calls for someone who understands our laboratory and sequencing processes and can integrate their data into the systems we use to run the lab. The analyst will bring operational discipline, provenance, and reproducibility to multi-project sequencing workflows, and will help train and coordinate junior personnel on data practices. Integration of Generative AI into these operational workflows is an ongoing lab priority and would be an important component of the role.

• Run, monitor, and help develop the lab's FoodSeq bioinformatics pipelines for processing DNA sequencing data, including metabarcoding assays (e.g., trnL, 12SV5) for dietary plant and animal species.
• Perform quality control on sequencing runs and processed data; identify sources of technical variation and flag adverse trends for the research team.
• Devise, test, and refine computational procedures and analytical techniques in the planning, development, and implementation of the lab's genomic data workflows.

• Organize and maintain sample, sequencing-run, and cohort metadata across multiple concurrent projects, ensuring accuracy, provenance, and chain-of-custody from sample receipt through analysis-ready output.
• Integrate sequencing data with the lab's sample-tracking, data-storage, and project-management systems, drawing on knowledge of the lab's molecular and sequencing methods to keep laboratory and computational records aligned.
• Manage data storage, organization, and access so that datasets are findable, well-documented, and reliably backed up.

• Produce processed datasets, summary outputs, and quality-control metrics that support the lab's manuscripts, grant deliverables, and downstream research.
• Evaluate and interpret data and prepare reports setting forth progress, trends, and conclusions; confer with the Principal Investigator on the design and interpretation of the lab's data workflows.

• Document workflows, maintain version-controlled code, and develop and improve standard operating procedures so that the lab's data operations are transparent and reproducible.
• Keep abreast of new tools and methods by reviewing scientific and technical literature, and help evaluate and adopt improvements to the lab's pipelines.

• Coordinate with wet-lab and field-sampling teams on data handoffs, sample readiness, and turnaround across projects.
• Train and help instruct students and junior personnel in data-management practices, computational workflows, and documentation standards.

Laboratory & Research Context: Familiarity with molecular biology or DNA sequencing workflows, and an interest in how research-data operations connect the lab's bench and field science to analysis-ready data.

Technical Proficiency: Experience working with high-throughput sequencing data and comfort at the command line, with scripting in Python, R, and/or bash. Familiarity with bioinformatics pipelines and amplicon/metabarcoding or metagenomic data analysis is a strong plus. A willingness to engage with and utilize Generative AI and attendant workflows (e.g., Claude Code/Codex, Linear, Slack, GitHub).

Rigor & Reproducibility: A meticulous, organized approach to data, code, and documentation, with a commitment to quality control and reproducible analyses.

Growth Mindset: A strong willingness to learn, adapt, and develop new skills, with curiosity and a proactive approach to problem-solving.

Communication Skills: Ability to communicate clearly within a team, document work for others, and contribute to a collaborative research environment.

Be part of a dynamic team committed to innovating in the fields of genomics, nutrition, and public health. Our lab offers a stimulating environment where your contributions will have a meaningful impact on scientific research and public health. We welcome applicants who are passionate about advancing genomic technologies and are ready to embark on a challenging and rewarding research career.

Bachelor's degree in biology, bioinformatics, computational biology, data science, or a related scientific or quantitative field, plus two years of relevant research experience (including research-data management, bioinformatics, or laboratory data operations), or an equivalent combination of education and experience. A related master's degree may offset required experience on a 1:1 basis.

Experience with high-throughput sequencing data, scripting (Python, R, and/or bash), version control (e.g., git), and bioinformatics pipelines (e.g., Snakemake/Nextflow, DADA2/QIIME, or comparable tools) is strongly preferred. Experience with AI-assisted coding workflows (e.g., Claude Code, Codex, etc.) is a plus.

Interested candidates should submit their resume and a cover letter detailing their suitability for the role, emphasizing their experience with sequencing data, computational workflows, and data management. Please apply through the Duke University careers portal, referencing this specific position.