The Senior Scientist will drive computational analysis of high-dimensional datasets, partnering closely with a well-integrated computational-experimental team to generate biological insights from complex genomic data. The ideal candidate has deep expertise in single-cell transcriptomics and epigenomics, experience handling large-scale datasets, and strong quantitative and programming skills. Experience in machine learning and AI approaches is highly desirable.

•          Ph.D in sciences or related field (Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field)
•          Minimum five years of experience with scientific investigation.
•          Must have demonstrated outstanding achievement in a field of research
•          Must have contributed to a number of peer-reviewed publications, inventions or the like, at least some of which have had a major impact on advancing the field or discipline.
•          Prior experience successfully securing external grant funding is highly desirable.
• Highly Preferred Experience:
  • Strong experience analyzing bulk and single-cell RNA-seq and epigenomic data
  • Proficiency in R, including common single-cell analysis frameworks
  • Experience working with large-scale genomic datasets and high-performance computing environments
  • Strong statistical background and data visualization skills
  • Experience analyzing DNA methylation data (e.g., array-based or sequencing-based approaches)
  • Experience analyzing long-read RNA sequencing datasets
  • Demonstrated use of machine learning/AI methods for genomic data integration or prediction
  • Familiarity with cloud-based workflows and reproducible pipeline development
  • Track record of publications in peer-reviewed journals

 

• Lead analysis of single-cell RNA-seq and multiome datasets (joint RNA/ATAC profiling)
• Perform integrative analysis across modalities, including bulk RNA-seq, ATAC-seq, and DNA methylation datasets
• Develop processing pipelines for novel single cell multiomic technologies
• Apply statistical modeling and machine learning methods to identify cellular states, regulatory programs, and epigenetic signatures
• Design and implement integrative multi-omic analyses across cohorts and experimental systems
• Present findings internally and contribute to publications and grant applications
• Stay current with emerging single-cell and AI-driven genomic analysis methodologies
• Performs other related duties.