United States onlyAbout Us
We are seeking a highly motivated Bioinformatics Analyst to join the research group of Dr. Robert Burk at Albert Einstein College of Medicine. Our group conducts molecular epidemiology and microbiome research with an emphasis on the human microbiome (cervicovaginal, oral, and gut) and HPV-related neoplasia. This role is central to translating large-scale sequencing datasets into rigorous, publication-ready results with direct relevance to chronic disease, cancer prevention and infectious disease research. Our work has been published in high-impact journals such as Nature Communications and Cell. The ideal candidate is an independent problem solver who is comfortable taking ownership of analysis workstreams, building and maintaining reproducible pipelines, and driving projects forward from raw data to interpretable outputs. They should be collaborative, communicate clearly with wet lab and epidemiology teams, and proactively propose analytic approaches that strengthen the science and accelerate progress.
POSITION RESPONSIBILITIES
- Pipeline development and leadership: Design, implement, document, and maintain end-to-end NGS pipelines for microbiome and HPV genomics applications (e.g., 16S/ITS1 amplicon sequencing, shotgun metagenomics, viral/HPV sequencing, bisulfite sequencing, and viral integration analyses).
- Large-scale data processing: Perform robust QC, read processing, reference alignment, taxonomic and functional profiling, strain-level analyses where appropriate, and reproducible reporting for cohort-scale datasets.
- Clinical and epidemiologic integration: Harmonize sequencing outputs with clinical and epidemiologic metadata (including complex longitudinal designs), perform data cleaning and validation, and generate analysis-ready tables.
- Statistical and computational analysis: Conduct and interpret statistical analyses using R and/or Python, including microbiome-specific methods (alpha/beta diversity, ordination, PERMANOVA, differential abundance) and epidemiologic modeling (regression and related approaches), with publication-quality visualizations.
- Project leadership and communication: Lead defined analysis workstreams, set realistic milestones, communicate risks and dependencies early, and present progress and results in lab meetings and to collaborators.
- Troubleshooting and optimization: Diagnose and resolve complex issues (pipeline failures, batch effects, contamination artifacts, inconsistent metadata, compute bottlenecks). Improve robustness, scalability, and runtime efficiency on shared compute environments.
- Reproducibility and best practices: Use version control (Git), structured documentation, and reproducible execution practices (workflow managers and/or containerization when appropriate). Maintain clear provenance from raw data to results.
- Scientific contribution: Propose fresh analytic ideas, evaluate new tools and methods, and contribute to interpretation and narrative framing of findings.
- Manuscripts and grants: Contribute figures, methods text, and analyses for manuscripts and grant applications.
QUALIFICATIONS
Required:
- Bachelor’s degree in bioinformatics, computational biology, biostatistics, epidemiology, computer science, or a related field; Master’s or PhD preferred.
- Strong programming ability in R and/or Python, plus comfort working in a Unix/Linux environment (shell scripting, HPC-style workflows).
- Demonstrated experience processing and analyzing NGS data, including building or extending pipelines rather than only running existing ones.
- Solid understanding of basic statistical concepts and the ability to translate scientific questions into appropriate analyses.
- Track record of independent problem solving, attention to detail, and producing reliable, well-documented outputs.
- Strong communication skills and a collaborative mindset for working with interdisciplinary teams.
Preferred:
- Experience in microbiome bioinformatics (16S, ITS1, shotgun metagenomics) and/or viral genomics with familiarity with phylogenetics, or integration-related analyses.
- Experience working with protected clinical or epidemiologic data and with best practices for data security and governance.
- Experience analyzing large cohorts and handling confounding, batch effects, and complex study designs (longitudinal, nested case-control, matched studies).
- Comfort translating analyses into clear figures, methods, and results text suitable for high-impact manuscripts
Practical Experience: The candidate is expected to have practical experience with bioinformatics work (handling NGS reads off the machine, etc.) and be willing and eager to adapt to new approaches. This role will be facilitated with a proactive attitude towards learning and applying new bioinformatics methods and technologies.
