We are seeking a highly motivated Staff Scientist to lead and support the analysis of large-scale molecular datasets in a dynamic, collaborative research environment. This role focuses on cutting-edge functional genomics, with an emphasis on single-cell and multi-omic technologies.

The Staff 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.

• 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

• Ph.D. in Biological Science or related field
• Three years experience

Preferred Skills

• PhD in Computational Biology, Bioinformatics, Genomics, Statistics, Computer Science, or related field (or equivalent 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