Research Data Analyst - 136273

The Moores Cancer Center (MCC) is one of just 57 NCI-designated Comprehensive Cancer Centers in the United States and the only one in San Diego County. As a consortium cancer center, it is a collaborative partnership between the UCSD: encompassing 28 departments, 6 schools (School of Medicine, Skaggs School of Pharmacy and Pharmaceutical Sciences, School of Public Health, Jacobs School of Engineering, School of Biological Sciences & the School of Physical Sciences), UCSD Health oncology hospitals and clinics; the basic and public health research and outreach of San Diego State University (SDSU), and the basic and translational research of the La Jolla Institute of Immunology (LJI). These various programs and units are all dedicated to fulfilling the Moores Cancer Center's mission of reducing cancer's burden. As such, it ranks among the top centers in the nation conducting the continuum of cancer research, providing advanced patient care, and serving the community through outreach and education programs. As a top-ranking, future-oriented organization, we offer challenging career opportunities in a fast-paced and innovative environment. Moores Cancer Center follows a progressive philosophy of career-path development for its employees including opportunities for cross-training, professional development, and progressive responsibility.

MCC's mission is to transform cancer care in our catchment area and beyond by driving exceptional scientific discoveries and innovations in prevention, detection, care, and training. MCC will make a global impact on improving health by reducing cancer burden through accelerated discovery and translation, compassionate and interdisciplinary care, education and community engagement, with the foundation of our core values - Excellence, Innovation and Risk-Taking, Collaboration, Diversity and Service.

Under the supervision of the Principal Investigator, the incumbent will conduct bioinformatics, statistical, and computational analyses to support laboratory research, with a primary focus on multi-omic data in cancer research. This includes the development and application of problem-oriented data analysis methods that integrate genomic, proteomic, clinical, and other complex datasets.

The incumbent will independently prepare and manage large-scale datasets, adapt and refine existing analytical methodologies, and develop novel statistical approaches as needed. Responsibilities also include contributing to manuscript preparation and submission, including drafting responses to reviewer comments for select studies.

This position requires the use of seasoned research skills and a thorough understanding of in-depth statistical analysis and/or research software programming techniques. The incumbent is expected to exercise sound judgment in selecting and applying appropriate methods and techniques to achieve research objectives, working independently within broadly defined practices and policies.

• Seven years of related experience, education/training, OR a Bachelor's degree in related area plus three years of related experience/training.

• Thorough knowledge of research function. Thorough knowledge of statistics, applied mathematics, bioinformatics, or related field. Theoretical knowledge of research function in Computational Biology, Genomics, or related scientific discipline. Thorough knowledge of oncology and/or multi-omics terminology including knowledge of cancer genomics, tumor microenvironment analysis, and pathway enrichment methods.

• Thorough skills associated with statistical analysis and systems programming. Demonstrated working experience with statistical methods and models incorporating a broad range of study designs and traditional and modern statistical approaches, as well as machine learning techniques. Experience to adapt existing methodology to new situations.

• Thorough skills in analysis and consultation. Demonstrated experience with data analysis, computer programming and graphing complex and custom statistical programming using statistical software. Proficiency in R, Python, and bioinformatics tools such as Seurat, Scanpy, salmon, kallisto, DESeq2, and limma-voom.

• Skills to communicate complex information in a clear and concise manner both verbally and in writing. Experience providing written interpretation of computational biology methods and results in a manner suitable for publication.

• Skills in project management. Excellent time management and organizational skills. Demonstrated skill in prioritizing multiple responsibilities and tasks. Ability to work in busy team-oriented environment and meet deadlines. Strong interpersonal and organizational skills.

• Research skills at a level to evaluate alternate solutions and develop recommendations. Demonstrated experience and a proven track record of successful application of a broad range of statistical data analysis methods, including generalized linear and additive models, mixed effects models and other multivariate methods, network analysis techniques, Bayesian methods, power and sample size calculation, statistical methods for genomics and sequence analysis (including next generation sequencing platforms).

• Enthusiasm for learning and a genuine interest in scientific research. Willingness and ability to learn new skills, research methods, and scientific principles. Good organizational skills and ability to maintain confidentiality. Demonstrated ability to read, comprehend and discuss research materials and protocols. Detail-oriented. Ability to work with minimal supervision. Ability to follow verbal and/or written instructions.

• MS or PhD.

• Demonstrated experience with high-performance computing environments and cloud-based genomic analysis. Demonstrated experience and expertise with multi-omic dataset queries, management, and quality assurance. Demonstrated experience in multi-omics data integration (e.g., epigenomics, proteomics). Familiarity with spatial transcriptomics and single-cell multi-omics approaches.

• Employment is subject to a criminal background check.