Senior Clinical Data Scientist

Intuitive•Sunnyvale, CA

1d•$152,000 - $257,200•Onsite

About The Position

The Senior Clinical Data Scientist will be part of Intuitive’s Ion business unit, which develops a robotic platform for minimally invasive biopsy in the peripheral lung. Working at the intersection of clinical science and data science, this role translates clinical questions into rigorous analyses that inform product strategy, evidence generation, and business decisions. The position reports to the Managing Staff, Clinical Data Science. The role partners with clinicians, data engineering, and cross-functional teams to shape and execute a clinical data science strategy. This includes developing hypotheses based on customer needs, selecting suitable data sources, performing quantitative analyses, and communicating findings that support patient outcomes and business objectives. The position requires clinical understanding of the Ion platform alongside hands-on expertise in modern data science methods and tools, with strong communication, presentation, and documentation skills. The role supports global clinical and economic evidence strategies, including clinical trial design, and helps prepare clinical datasets for regulatory submissions. It also contributes to a data-driven culture by producing reproducible, well-documented analyses and evidence for internal and external audiences.

Requirements

At least 6 years of experience with a master's degree, or at least 4 years with a PhD, in a quantitative, scientific, or engineering field; an MD with quantitative research experience is also accepted.
Experience using 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 or registry data, and claims/EHR-style databases.
Experience applying causal inference in a healthcare or medical device context.
Experience with platforms such as Snowflake or Databricks for production-level data pipelines and analytics.
Experience with data visualization tools such as Tableau to present complex data to non-technical stakeholders or regulatory bodies.
Experience with Electronic Data Capture systems such as Medrio for managing clinical study or registry data.
Experience using AI/ML tools to accelerate software development and data analysis workflows.

Nice To Haves

Strong critical thinking and analytical skills, including the ability to turn ambiguous clinical questions into testable hypotheses and select appropriate methods.
Strong organizational skills, particularly in code management and documentation.
Ability to work effectively across clinical, engineering, regulatory, commercial, and data science teams.
Self-starter who can manage multiple priorities, work independently, and deliver amid 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, and data privacy regulations such as HIPAA and GDPR.
Familiarity with the lung cancer landscape, including screening, diagnosis, and treatment.

Responsibilities

Work with internal and external stakeholders, including clinicians, administrators, and policymakers, to identify clinical data needs and translate them into prioritized research questions and testable hypotheses.
Design, implement, and validate quantitative analyses in Python or R using appropriate statistical, machine learning, and causal inference methods on device logs, clinical studies and registries, insurance claims, and EHR/EMR data.
Translate analyses into decision-useful narratives for technical peers, senior leadership, and external physicians.
Demonstrate the impact of analyses through reports, presentations, dashboards, peer-reviewed publications, and customer-facing deliverables.
Partner with cross-functional teams on global clinical and economic evidence strategies, including the design and support of clinical trials.
Support regulatory submissions by structuring and validating clinical datasets to meet FDA and EU MDR compliance standards.
Create and maintain reproducible codebases with clear documentation, version control, and consistent coding standards.
Promote the use of data to generate evidence and improve products.