Data Scientist

About the job

Job Description

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.