Data Scientist

It started with a simple idea: what if surgery could be less invasive and recovery less painful? Nearly 30 years later, that question still fuels everything we do at Intuitive . As a global leader in robotic-assisted surgery and minimally invasive care , our technologies—like the da Vinci surgical system and Ion —have transformed how care is delivered for millions of patients worldwide.

We’re a team of engineers, clinicians, and innovators united by one purpose: to make surgery smarter, safer, and more human. Every day, our work helps care teams perform with greater precision and patients recover faster, improving outcomes around the world.

The problems we solve demand creativity, rigor, and collaboration. The work is challenging, but deeply meaningful—because every improvement we make has the potential to change a life.

If you’re ready to contribute to something bigger than yourself and help transform the future of healthcare , you’ll find your purpose here.

Primary Function of Position

This position is part of the evidence strategy and research function supporting da Vinci platforms and applies data science, statistical, and analytical methods to generate scientific evidence about the performance, use, and impact of Intuitive products, technologies, and procedures for the Cardiac Surgery Program. The Data Scientist partners with scientific, medical, clinical, research, data, product, and engineering teams to address evidence gaps with rigorous analytical approaches. The role works with complex surgical, robotic system, product-generated, clinical, financial, and other data sources to support retrospective and prospective research. The Data Scientist contributes across the research lifecycle, from research question development and data feasibility through analysis, interpretation, and scientific dissemination.

Essential Job Duties

This position has responsibility and authority for the following:

• Partner with scientific, medical, clinical, research, product, engineering, and evidence stakeholders to translate evidence needs into clear research questions, study designs, analytical plans, and appropriate data science approaches.

• Design, execute, and interpret analyses using da Vinci system, product, procedural, research, clinical, financial, and other relevant healthcare data.

• Evaluate and apply appropriate statistical and data science methods, including descriptive and inferential statistics, longitudinal analysis, experimental and quasi-experimental methods, causal inference, predictive modeling, and machine learning, based on research questions and available data.

• Conduct data exploration and feasibility assessments to characterize datasets, evaluate data quality and fitness for use, identify potential sources of bias or confounding, and inform research design and evidence-generation opportunities.

• Provide analytical expertise across retrospective and prospective research studies, including development of hypotheses, endpoints, feasibility assessments, statistical analysis plans, derived variables, metrics, and analytical methods.

• Develop reproducible analytical workflows using Python, SQL, and other appropriate tools, and collaborate with data engineering, data management, and technical teams to establish analysis-ready datasets and understand data provenance and limitations.

• Interpret and communicate analytical methods, findings, uncertainty, limitations, and implications to scientific, clinical, technical, business, and external research stakeholders.

• Collaborate with internal and external research professionals, clinicians, and scientists to conduct research projects and dissemination research findings.

• Contribute to scientific evidence-generation and dissemination deliverables, including research reports, abstracts, posters, manuscripts, conference presentations, and collaborations with external investigators and research institutions.

Required Skills and Experience

• Minimum of 5 years of related experience with a Bachelor's degree; 3 years of related experience with a Master's degree; a PhD without prior professional experience; or equivalent work experience.

• Related experience applying data science, statistics, biostatistics, biomedical or health informatics, or other quantitative methods to complex research, healthcare, engineering, or product data.

• Strong quantitative foundation and knowledge of statistical methods, including regression, hypothesis testing, uncertainty estimation, bias, confounding, and interpretation of observational data.

• Proficiency in Python and SQL for data manipulation, analysis, visualization, and development of reproducible analytical workflows.

• Experience working with large, complex, longitudinal, and/or high-dimensional datasets and evaluating data quality and limitations.

• Demonstrated judgment in selecting analytical methods and techniques to address research questions and resolve moderate to complex analytical problems.

• Strong oral and written communication skills with ability to communicate complex statistical, technical, and scientific concepts clearly and effectively to audiences with varying levels of technical expertise and to build productive cross-functional relationships.

• Strong scientific and technical writing, organization, and collaboration skills, with the ability to manage multiple analytical projects and priorities with limited day-to-day direction.

Required Education and Training

• Bachelor's degree in Data Science, Statistics, Biostatistics, Computer Science, Biomedical or Health Informatics, Engineering, Applied Mathematics, Epidemiology, or a related quantitative discipline, or equivalent work experience.

• Training or demonstrated experience in statistical analysis, data science, research methodology, or related quantitative methods.

Working Conditions

• Ability to work in healthcare and surgical environments, including human or animal laboratories, as needed.

• Ability to travel up to 25% of the time.

Preferred Skills and Experience

• Master's degree or PhD in Data Science, Statistics, Biostatistics, Biomedical or Health Informatics, Computer Science, Epidemiology, Engineering, or a related quantitative discipline.

• Experience applying data science or statistical methods in healthcare, medical devices, biomedical research, clinical research, or life sciences.

• Experience analyzing surgical, medical-device, robotic-system, clinical, or other real-world healthcare data.

• Experience with observational research, causal inference, longitudinal analysis, experimentation, or quasi-experimental study designs.

• Experience applying machine learning methods to scientific, healthcare, medical-device, or robotic-system data.

• Experience contributing to peer-reviewed publications, scientific abstracts, posters, or conference presentations.

• Experience collaborating with clinicians, surgeons, investigators, academic institutions, or other external research partners.

• Familiarity with human-subjects research, Good Clinical Practice, healthcare data privacy requirements, software engineering practices, APIs, or modern data platforms.