Senior Clinical Data Scientist

Senior Clinical Data ScientistThe Senior Clinical Data Scientist will be part of the Ion business unit. The Ion endoluminal system is Intuitive's robotic platform for minimally invasive biopsy in the peripheral lung, with an initial goal of improving the early diagnosis of lung cancer. The Senior Clinical Data Scientist sits at the intersection of clinical science and data science within the Ion business unit, translating clinical questions into rigorous, data-driven analyses that inform product strategy, evidence generation, and business decisions. This role reports to the Managing Staff, Clinical Data Science.The individual in this role will partner with clinicians, cross-functional teams, and data engineering to design and execute a clinical data science strategy — defining hypotheses grounded in customer needs, identifying appropriate data sources, conducting quantitative analyses, and communicating results that demonstrably impact patient outcomes and business objectives. Unlike a purely clinical or purely technical role, this position demands both deep clinical understanding of the Ion platform and hands-on proficiency in modern data science methods and tools.Success requires a strong technical foundation in Python or R, statistics, and causal reasoning; the ability to navigate ambiguity in a fast-evolving clinical and commercial landscape; and exceptional skill in communicating complex findings clearly — especially through polished visual presentations and well-organized documentation.Essential job duties include working with internal and external stakeholders to identify data-centric needs in the clinical space and translate them into prioritized research questions with testable hypotheses; designing, implementing, and validating quantitative analyses in Python or R; translating analyses into decision-useful stories, tailoring them to diverse groups of stakeholders; demonstrating impact through internal/external reports, presentations, dashboards, peer-reviewed publications, and customer-facing deliverables; partnering with cross-functional teams to assist with the design and execution of global clinical and economic evidence strategies, including designing and supporting clinical trials; supporting regulatory submissions by structuring and validating clinical datasets to meet FDA and EU MDR compliance standards; creating and maintaining well-organized, reproducible codebases with clear documentation, version control, and consistent coding standards; and catalyzing a data-driven culture internally — championing the use of data to generate compelling evidence and ultimately build a better product.Required skills and experience include 6+ years of experience with a Master's degree or 4+ years with a PhD in a quantitative, scientific, or engineering field (e.g., biostatistics, epidemiology, data science, computer science, biomedical engineering, public health, or a related discipline). MD with quantitative research experience also accepted. Experience with Python or R for data analysis, statistical modeling, and visualization. Working knowledge of SQL for querying and managing structured datasets. Demonstrated experience with causal inference methods and study design. Experience working with device-generated log data, clinical study/registry data, and claims/EHR-style databases. Experience with causal inference in a healthcare or medical device context. Experience using platforms such as Snowflake or Databricks for production-level data pipelines and analytics. Experience with data visualization tools (e.g., Tableau) for the presentation of complex data to non-technical stakeholders or regulatory bodies. Experience with Electronic Data Capture (EDC) systems (e.g., Medrio) for managing clinical study/registry data. Experience using AI/ML tools to accelerate software development and data analysis workflows.Preferred skills and experience include excellent critical thinking and analytical skills, with the ability to frame ambiguous clinical questions as testable hypotheses and select fit-for-purpose methods; stellar organizational skills, particularly with respect to code management and documentation; ability to work effectively with cross-functional teams, including clinicians, engineers, regulatory, commercial, and data science partners; self-starter attitude with the ability to manage multiple priorities, operate independently, and deliver under ambiguity; track record of peer-reviewed publications or conference presentations in clinical research or data science; experience in medical devices or other regulated industries; familiarity with Good Clinical Practice (GCP), ISO 14155 (clinical investigation of medical devices), and data privacy regulations (HIPAA/GDPR); and familiarity with the lung cancer landscape (screening, diagnosis, treatment).