Senior Computational Scientist - Center for Translational Science Reports

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At HonorHealth, you'll find something special. From humble beginnings in 1927 to one of Arizona's largest nonprofit healthcare systems, our culture is built on warmth and neighborly kindness. Behind every smile is a highly skilled professional with deep expertise and an unwavering dedication to what matters most - caring for the health and well-being of people and communities across the greater Phoenix area.

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Job Summary

A Senior Computational Scientist leads complex, interdisciplinary research initiatives, sets technical direction for computational projects, and drives contributions to high-impact publications and grant applications. The role provides scientific and technical leadership, mentors junior scientists, and collaborates closely with clinicians, biologists, bioinformaticians, and AI/ML experts to align computational analyses into meaningful biological and translational insights. This position also ensures clear and effective communication of findings through presentations, peer-reviewed manuscripts, data-sharing deliverables, and sponsored research reports.

Essential Functions

• Implement, apply, maintain, and customize available informatics resources and scientific software/data sources across multiple programming languages and platforms for research support. Develop front-end interfaces for other analysis tools, visualize and compile results, and facilitate routine data processing, migration, organization, and management. Build project- specific workflows that automate analysis steps.
• Design, prototype, and evaluate innovative computational algorithms, data analysis, and statistical methods for biomedical data processing, interpretation, and knowledge extraction from data, computational algorithms, databases, and scientific computing, including large-scale data analysis. Suggest and explore alternative or multiple data processing approaches.
• Following the scientific literature and manuscripts, investigate new biomedical data processing methods, trends, technologies, and paradigms relevant to biomedical informatics, providing feedback on scientific findings and contributing to manuscripts, grants, research proposals, and research planning. Attend, contribute to, and present at internal and external scientific meetings and conferences.
• Manage multiple bioinformatics collaborations and projects while mentoring junior staff. Engage in strategic planning, organization, management, utilization, and optimization of current and future bioinformatics resources, skills, and requirements. Collaborate with PIs, researchers, and other bioinformaticians to coordinate and steer bioinformatics activities, reinforcing the organization's competitive edge in biomedical research. Work with other research divisions, computing infrastructure staff, and external academic and industrial partners.
• Performs other duties as assigned.
• The above job responsibilities describe the general nature and level of work to be performed. It does not restrict management's right to assign or re-assign duties at any time.

Education

Ph.D. in Bioinformatics, Computer Science, Systems Biology, Computational or Quantitative Biology, Biostatistics, Statistics, Applied Mathematics, or a related field - Required

Experience

Must have experience in genomics and the use of NGS long/short-read sequencing technologies as well familiarity with other molecular and physiological ('omic') assay data types; the use of high-performance computing to handle large biological datasets; the use of large biological datasets (e.g., UK Biobank); the use of programming languages such as Perl, C++, Python, R, and MATLAB to perform machine learning and statistical analysis; the use of bioinformatics software such as Plink to handle genetic and DNA sequencing data; and in software engineering such as version control (CVS/SVN), backup, documentation, testing, and validation - Required

At least 8 years of experience, involving systems biology, biostatistical analysis, and designing and implementing prediction models and algorithms to assess risks for various diseases using genetic and other data types - Preferred

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