Summary

The Stem Cell and Regenerative Medicine (STaR) Center at Baylor College of Medicine (https://www.bcm.edu/research/research-centers/star-center) focuses on mechanisms regulating stem cell self-renewal and differentiation across developmental stages and tissue types. Baylor College of Medicine is widely recognized for its exceptional, innovative, and collaborative research environment. It is investing significant resources in expanding interdisciplinary research in the computational, genomic, cellular, and molecular bases of human health and disease. The labs in the STaR center apply state-of-the-art computational strategies to contribute significantly to biomedical research while fostering career development.  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 Staff Scientist position is available immediately in the STaR center. The Senior Staff Scientist will independently design and lead computational approaches to research problems related to epigenetics and RNA biology in health and disease. They will take ownership of highly interdisciplinary projects and apply state-of-the-art open-source software for basic and advanced analysis of next-generation sequencing data. The focus will be on single-cell genomics, RNA/protein interactions, RNA translation, and chromatin organization. Will also be expected to establish analytical pipelines, set computational standards across collaborating groups, and mentor junior scientists and trainees in best practices. In addition, they will have opportunities to present research findings at conferences and network with colleagues. The ideal candidate will be highly motivated, well-organized, manage time effectively, and work independently with minimal supervision while contributing as part of a team. They will bring deep, demonstrated expertise in bioinformatics, computational biology, and biostatistics, with advanced proficiency in languages such as Python and R, and strong command of Linux/Unix and high-performance computing (HPC) for the analysis of genomics data. 

Job Duties

• Independently design, develop, and maintain computational pipelines for the analysis of CUT&TAG, RNA-seq, ATAC-seq, single-cell RNA-seq, single-  cell multiomics, CLIP-seq/eCLIP, and ribosome profiling data. 
• Select, apply, and develop appropriate statistical and machine learning methods to interpret these datasets, and establish analytical standards and best practices across collaborating groups. 
• Lead the integration of multi-omic datasets in partnership with wet lab scientists, advising on experimental design and statistical approaches. 
• Maintain reproducible, well-documented computational workflows, including version-controlled code and detailed records of analyses. 
• Critically evaluate scientific literature and emerging computational methods to guide and advance research projects. 
• Co-author scientific papers and contribute to the writing of grant applications. 
• Mentor and train junior bioinformaticians, students, and wet lab scientists in computational methods and best practices. 
• Interact effectively with team members across multiple groups to drive progress on complex problems while ensuring a high standard of scientific rigor. 
• Perform other job related duties as assigned.

Minimum Qualifications

• Doctoral Degree. Experience may not be substituted in lieu of degree.
• Seven years of postdoctoral  research experience.

Preferred Qualifications

• Extensive, hands-on experience analyzing several of the following: CUT&TAG, RNA-seq, ATAC-seq, single-cell RNA-seq, single-cell multiomics, CLIP-seq/eCLIP, and ribosome profiling. 
• Demonstrated experience developing or maintaining reproducible analytical pipelines and version-controlled code (e.g., Git, Snakemake/Nextflow).
• Demonstrated ability working with open-source bioinformatics software and integrating multi-omic datasets.
• Proficiency in shell scripting and familiarity with statistical and/or machine learning methods applied to genomics data.
• Experience mentoring or training junior scientists in computational methods.
• 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.
• Four years of relevant bioinformatics or computational biology experience.
• Demonstrated proficiency in Linux/Unix environments, cluster-based (e.g., SLURM) high-performance computing, and programming in Python and R.