Summary

The Di Stefano Lab at Baylor College of Medicine's Stem Cell and Regenerative Medicine (STaR) Center and Center for Cell and Gene Therapy investigates the post-transcriptional mechanisms that govern stem cell potency and cell fate — with implications for regenerative medicine and cancer biology. We work with human and mouse embryonic stem cells and adult stem cells, both in vitro and in vivo, combining genome-wide sequencing approaches and CRISPR-based perturbations to dissect how RNA processing, translation, and chromatin regulation control developmental transitions and cell identity. Baylor College of Medicine is widely recognized for its exceptional, innovative, and collaborative research environment. The STaR center is in the Texas Medical Center, the largest biomedical complex in the world, immediately adjacent to Houston's vibrant Museum District and Hermann Park. A Senior Bioinformatics Analyst position is available in the Di Stefano Lab. The individual will identify and implement computational solutions to research problems at the intersection of epigenetics and RNA biology. They will take on highly interdisciplinary projects and apply state-of-the-art open-source software to basic and advanced analyses of next-generation sequencing data, with a focus on single-cell genomics and multi-omics, RNA translation, and epigenetics. In addition, they will have opportunities to present research findings at conferences and network with colleagues across the Texas Medical Center and beyond. The ideal candidate will be highly motivated, well-organized, able to manage time effectively, and capable of working both independently and as part of a team. They will obtain fundamental, career-building exposure in bioinformatics, computational biology, and biostatistics as they develop scripts in Python and R, while using Linux/Unix and high-performance computing (HPC) to analyze genomics data.

Job Duties

• Process, analyze, and interpret multi-omic datasets across CUT&Tag, RNA-seq, ATAC-seq, single-cell RNA-seq, single-cell multiomics, CLIP-seq, and ribosome profiling, from raw reads through biological conclusions.
• Develop and apply statistical and machine learning approaches tailored to each data type, including differential expression, chromatin accessibility, translational efficiency, and cross-modal data integration.
• Build and maintain reproducible, well-documented bioinformatics pipelines using workflow management systems (e.g., Snakemake, Nextflow) and version control (Git/GitHub), deployable on HPC environments.
• Collaborate closely with wet lab scientists to co-design experiments, integrate computational and molecular findings, and drive projects toward publication.
• Co-author peer-reviewed manuscripts and contribute to grant applications.
• Search and critically evaluate primary scientific literature to inform analytical decisions and identify emerging methodologies relevant to the lab's research program.
• Communicate results clearly to interdisciplinary audiences, including biologists, trainees, and external collaborators.
• Mentor graduate students and postdoctoral fellows in computational methods and analytical best practices.

Minimum Qualifications

• Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
• One years of relevant experience.

Preferred Qualifications

• Master’s or PhD degree in a related field
• Demonstrated experience in Linux operating systems and SLURM cluster-based computing
• Proficiency in programming languages such as Python, Shell Scripting, and R
• Demonstrated ability working with open-source bioinformatics software
• Experience analyzing CUT&TAG, RNA-seq, ATAC-seq, and/or single-cell RNA-seq
• A record of taking initiative to solve problems and working to high-quality standards
• Attention to detail and accurate record keeping
• Ability to multitask, work and learn independently, and be self-motivated