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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager - Data Science - **Company:** Caris Life Sciences - **Location:** Tempe, AZ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Bioinformatics, Clinical Data Repository, Cloud Computing, Software Quality, Computational Biology, Data Visualization, Digital Assets, Python (Programming Language), Machine Learning, High Performance Computing, Information Technology, Machine Learning Operations - **Published:** September 3, 2026 - **Apply:** https://dejobs.org/x/x/70315FFA677C4439BBA0CC381C3C7DBF/job/ ## About the Role * PhD in data science, statistics, biostatistics, computer science, computational biology, bioinformatics, or a related quantitative field. * Five or more years of relevant experience, including project, team, or people leadership in biomedical data science. * Demonstrated experience leading data science projects from problem definition through validated delivery. * Advanced proficiency in Python and working proficiency in SQL. * Strong foundation in statistical modeling, machine learning, data visualization, and scientific interpretation. * Experience analyzing large, complex biomedical datasets, such as genomic, proteomic, clinical, or other multimodal data. * Experience developing reproducible analytical workflows with appropriate validation, documentation, and quality controls. * Ability to review technical work and mentor data scientists. * Strong written and verbal communication skills, including the ability to explain nuanced technical material to varied audiences. * Ability to coordinate competing project priorities and deliver high-quality work in a collaborative environment., * Experience in oncology, precision medicine, or immunology. * Experience integrating multimodal molecular and clinical data. * Experience developing molecular signatures, derived data assets, research deliverables, data products, or clinical decision support analyses. * Experience in an industry or customer-facing environment. * Experience with cloud computing or high-performance computing. * Familiarity with production machine-learning or MLOps practices. * Experience developing agentic AI applications or workflows, including agent orchestration, tool integration, evaluation, and human oversight. Physical Demands * Ability to work at a computer for extended periods. ## Description The Manager of Data Science provides hands-on technical, scientific, and team leadership for assigned data science projects and major workstreams supporting research, product development, commercial initiatives, and customer-facing priorities. This role coordinates analytical plans, reviews, timelines, and deliverables while ensuring that work follows established standards for scientific rigor, reproducibility, validation, and documentation. The Manager works within priorities and practices established by Data Science leadership and partners closely with scientific, technical, product, and commercial teams., * Manage and mentor an assigned team of data scientists, including coordinating day-to-day priorities and supporting professional development. * Own assigned data science projects and major workstreams from analytical planning through validation and delivery. * Develop project plans and coordinate reviews, dependencies, handoffs, timelines, and success criteria. * Translate scientific, clinical, product, and commercial questions into appropriate analytical approaches and deliverables. * Apply and reinforce established standards for reproducibility, validation, documentation, code quality, and statistical and machine-learning analyses. * Review analytical methods, code, validation results, and model artifacts for scientific rigor and quality. * Facilitate analytical vetting with computational biology, bioinformatics, translational science, clinical subject-matter experts, and other scientific partners. * Coordinate cross-functional work with Engineering, Product, Commercial, Business Development, Clinical Decision Support, and related teams. * Identify project-level priority or resource conflicts and escalate them to Data Science leadership when needed. * Communicate analytical methods, limitations, progress, and results to technical and nontechnical stakeholders. * Evaluate relevant advances in data science, statistics, and machine learning for use within assigned projects. * Improve team-level delivery practices to increase quality, throughput, predictability, and partner trust., * Job-specific, safety, and compliance training will be assigned based on the responsibilities of the position. 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