Bioinformatics Analyst, Senior

Overview The Senior Bioinformatics Analyst is part of the Creative Data Solutions (CDS) Shared Resource at Vanderbilt University and serves as a senior-level contributor responsible for leading bioinformatics analyses of genomic data sets, developing and implementing advanced transcriptomic statistical models, and directing research studies. This position requires that the ideal candidate will have extensive experience in analyzing large-scale genomic and transcriptomic data, including next-generation sequencing data, and deep expertise in statistical analysis methods involved with such data. The candidate should demonstrate leadership capabilities, mentor junior team members, and possess exceptional communication and problem-solving skills. Reporting directly to the Director of CDS, the Senior Bioinformatics Analyst will lead collaborations with investigators throughout the Vanderbilt community on multiple complex projects simultaneously. About the Work Unit: Creative Data Solutions (CDS), a Vanderbilt University Shared Resource, provides informatics and bioinformatics services to investigators in the fields of academic and clinical research, with an emphasis on life sciences. CDS blends modern data science approaches with robust software and database engineering to provide practical solutions to problems and challenges faced by research investigators in a timely and cost-effective fashion. Key Functions and Expected Performance Acts as a specialist in the analysis and interpretation of large-scale genomic and transcriptomic data sets using advanced bioinformatics tools and methods. Modalities include both bulk and single-cell RNA-Seq, DNA-Seq, chromatin binding analysis (CUT&RUN, ChIP-Seq, ATAC-Seq, etc.), spatial transcriptomics (10X Visium/HD, Nanopore GeoMX), and ad hoc bioinformatics. Designs and develops complex sophisticated statistical models. Leads the design and execution of research studies involving genomic and transcriptomic data. Directs groups to conduct collaborative efforts with team members to interpret and present research findings. Maintains expertise in the latest developments in bioinformatics, genomics, and transcriptomics. Architects and maintain robust pipelines for processing and analyzing large-scale genomic data sets. Supervises and coordinates with bioinformatics and laboratory personnel to ensure the quality and accuracy of genomic data. Leads the interpretation and visualization of genomic data and prepares comprehensive reports and presentations for research studies and publications. Drives the development of novel analytical methods and techniques for genomic data analysis. Provides strategic bioinformatics leadership for clinical studies and trials involving genomic data. Leads the development and implementation of data management and sharing policies for genomic data. Mentors and trains team members and collaborators on advanced bioinformatics tools and methods. Represents the organization at scientific conferences, workshops, and training programs to maintain cutting-edge knowledge in bioinformatics, genomics, and transcriptomics. Coordinates externally with partners and stakeholders, such as academic and industry researchers, regulatory agencies, and patient advocacy groups, on research projects and initiatives. Handles project timelines, resource allocation, and deliverable quality across multiple concurrent projects, while auditing processes to ensure compliance and efficiency. Supervisory Relationships This position does not have supervisory responsibilities; this position reports administratively and functionally to the Director of CDS. Education and Certifications Ph.D. degree in bioinformatics, computational biology, genetics, statistics, or a related field is required with at least 8 or more years of experience Experience and Skills Post-doctoral experience or skill is preferred. Experience with BioVU would be an added benefit. Experience with long read RNA-Seq would be a strong advantage. Background or experience in bioinformatics and a strong understanding of statistics is required. Proficiency in programming languages R and Python Knowledge of statistical analysis methods for genomic data, including GWAS, rare variant analysis, and eQTL analysis. Familiarity with common bioinformatics tools and databases, such as Ensembl, UCSC Genome Browser, and dbGaP. Familiarity with HPC and cloud computing infrastructure. Excellent communication and problem-solving skills. Ability to work independently and as part of a team #J-18808-Ljbffr