The posting
At Caris, we understand that cancer is an ugly word—a word no one wants to hear, but one that connects us all. That’s why we’re not just transforming cancer care—we’re changing lives.
We introduced precision medicine to the world and built an industry around the idea that every patient deserves answers as unique as their DNA. Backed by cutting-edge molecular science and AI, we ask ourselves every day: “What would I do if this patient were my mom?” That question drives everything we do.
But our mission doesn’t stop with cancer. We're pushing the frontiers of medicine and leading a revolution in healthcare—driven by innovation, compassion, and purpose.
Join us in our mission to improve the human condition across multiple diseases. If you're passionate about meaningful work and want to be part of something bigger than yourself, Caris is where your impact begins.
Position Summary 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.
Job Responsibilities
- 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Training
- Job-specific, safety, and compliance training will be assigned based on the responsibilities of the position.
Other
- Occasional travel may be required.
- Occasional evening or weekend work may be required.
Conditions of Employment: Individual must successfully complete pre-employment process, which includes criminal background check, drug screening, credit check ( applicable for certain positions) and reference verification.
This job description reflects management’s assignment of essential functions. Nothing in this job description restricts management’s right to assign or reassign duties and responsibilities to this job at any time.
Caris Life Sciences is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, gender identity, sexual orientation, age, status as a protected veteran, among other things, or status as a qualified individual with disability.
