Summary

A Bioinformatics Analyst I position is available in the laboratory of Dr. Hari Yalamanchili at the Data Science Hub within the Jan and Dan Duncan Neurological Research Institute (NRI) at Baylor College of Medicine. We are seeking a motivated and detail-oriented scientist with strong computational skills and interest in high-throughput genomics data analysis. The successful candidate will develop and apply bioinformatics pipelines to analyze large-scale multi-omic datasets, including genomic, transcriptomic, and epigenomic data. The analyst will work closely with computational and experimental scientists and contribute to collaborative research projects in neurological disorders. This position provides opportunities to expand expertise in bioinformatics, statistical analysis, and integrative multi-omics research.

Job Duties

• Process and analyze high-throughput Next Generation Sequencing datasets including RNA-seq, ChIP-seq, WGBS, and single-cell datasets.
• Develop, implement, and maintain bioinformatics analysis pipelines.
• Support integrative analysis of multi-omic datasets.
• Maintain computational infrastructure and software environments.
• Work closely with collaborators and present analytical results.
• Prepare analysis reports and documentation and assist with drafting manuscripts for publication.

Minimum Qualifications

• Bachelor's degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field.
• No experience required,

Preferred Qualifications

• MS in Bioinformatics, Computer Science, Statistics, or a related field.
• Experience with programming languages such as R, Python, Perl or equivalent.
• Experience working in a UNIX/Linux environment.
• At least one year of programming or bioinformatics experience.
• Strong written and verbal communication skills.
• Working knowledge of genomics and Next Generation Sequencing data analysis.
• Understanding of statistics and machine learning approaches.
• Experience with HPC or cloud computing environments (AWS or equivalent).
• Working knowledge of machine learning, deep learning, and large language model (LLM) frameworks.