Managing Staff, Clinical Data Science

Company Description

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.

Job Description

Primary Function of Position

The Managing Staff, Clinical Data Science role leads a small, highly technical team responsible for extracting insight from diverse healthcare data to inform product, clinical, and business decision-making. This role combines people, technical, and strategic leadership, in an area with substantial ambiguity and evolving priorities.

The individual in this role will oversee a small team of scientists and/or technical contributors working across clinical study data, device and system log files, electronic medical records, HEOR databases, and other structured and unstructured datasets. He/she will define the team’s technical direction, ensure strong analytical and engineering practices, and build the infrastructure, methods, and operating model needed to deliver high-quality, decision-relevant analyses at scale.

This leader will work closely with stakeholders across Medical Affairs, Clinical Affairs, Market Access, HEOR, product management, engineers, R&D, regulatory, commercial and other cross-functional teams to shape strategy, generate publications, and contribute to product features. Success in this role requires strong technical judgment, excellent communication and stakeholder management skills, and the ability to create clarity and momentum in a high-ambiguity environment.

The core function includes translating complex biological, procedural, and device-generated data into rigorous clinical & health-economic evidence. This includes simple trial data management all the way to advanced predictive modeling and evidence generation.

Essential Job Duties 

• Team Leadership and People Management
  • Lead, coach, and develop a technical team of individual contributors.
  • Set clear goals, priorities, and expectations for the team, while supporting individual growth and career development.
  • Mentor team members on technical topics, including analytical methods, data interpretation, and reproducible workflows.
  • Build a strong team culture grounded in scientific rigor, collaboration, accountability, and continuous improvement.
  • Allocate resources effectively across competing priorities and evolving business needs
• Evidence Generation
  • Trial Design Support: Collaborating with Clinical Affairs and Principal Investigators to define data collection strategies, endpoints, and statistical analysis plans (SAPs) for pre-market and post-market trials.
  • HEOR Integration: Generating the specific data cuts and statistical models required by HEOR teams, crucial for building cost-effectiveness models and securing positive HTA for market access in various regions.
  • Regulatory Submissions: Structuring and validating clinical datasets to meet strict FDA and EU MDR compliance standards, in collaboration with Clinical Affairs.
• Advanced Analytics on Device-Generated Data
  • Analyze and extract the clinical meaning from telemetric and procedural data.
  • Build ML models to analyze procedural, kinematic, imaging data to identify factors that correlate with successful patient outcomes.
  • Identifying novel digital (and traditional) biomarkers that can predict procedural success or complication risks.
• Real-World Evidence (RWE) & Post-Market Surveillance
  • Longitudinal data tracking and analysis from registries, EMR, and device telemetry to monitor long-term safety and performance.
  • Develop automated statistical methods to detect early signals of adverse events.
• Inform Product Roadmap
  • Translate clinical data insights into actionable feedback for the product development teams, directly influencing the 8 year product roadmap and feature release timeli