Managing Staff, Clinical Data Science

Intuitive•Peachtree Corners, GA

1d•$182,800 - $309,400•Remote

About The 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.

Requirements

Significant professional experience in data science, research science, biostatistics, computational science, or a related discipline, including experience leading technical teams.
Strong experience working with complex, real-world datasets, ideally including clinical study data and other healthcare-related data sources.
Experience designing and implementing robust analytical workflows and technical best practices.
Strong stakeholder management skills, with the ability to work effectively across multiple functions and levels of the organization.
Proven ability to define strategy and drive execution in areas with incomplete information or evolving direction.
Excellent written and verbal communication skills, including the ability to explain complex technical concepts clearly and concisely.
Expert-level Python or R, and SQL
Advanced degree in a quantitative, scientific, or engineering field (e.g., PhD, MS, MPH, MD, or equivalent experience).

Nice To Haves

Experience working with clinical study data, HEOR or claims/EHR-style databases, and machine/device-generated log data.
Experience with Good Clinical Practice, ISO 14155 (clinical investigation of medical devices), and data privacy regulations (HIPAA/GDPR).
Experience building or overseeing analytical infrastructure in a regulated or quality-sensitive environment.
Familiarity with healthcare, medical device, diagnostics, or life sciences applications.
Experience supporting evidence generation, product development, or cross-functional decision-making using diverse data assets.
Strong understanding of reproducible research, data governance, and scalable analytics in collaborative environments.
Experience with Electronic Data Capture (EDC) systems (like Medidata or Castor) and advanced visualization tools to present complex data to non-technical stakeholders or regulatory bodies.

Responsibilities

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
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.
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.
Structuring and validating clinical datasets to meet strict FDA and EU MDR compliance standards, in collaboration with Clinical Affairs.
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.
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.
Translate clinical data insights into actionable feedback for the product development teams, directly influencing the 8 year product roadmap and feature release timelines for next-generation system
Establish and maintain best practices for data management, analytics, code quality, reproducibility, documentation, and validation.
Ensure the team uses fit-for-purpose methods and scalable infrastructure to support analyses across diverse data sources.
Guide the design and evolution of the team's analytical environment, tools, and workflows to improve efficiency, quality, and reusability.
Guide the technical infrastructure. Partner with data engineering to design how clinical data flows from the hospital setting (or trial EDC systems) into secure, compliant internal databases.
Provide technical oversight on complex analyses, ensuring outputs are scientifically sound, clearly communicated, and decision-useful.
Partner with stakeholders to understand near-, mid-, and long-term business priorities.
Identify the key decisions, questions, and uncertainties that require analytical support.
Guide technical teams in applying rigorous methods to generate insights that inform business decisions.
Balance and prioritize competing short- and long-term projects.
Anticipate future data, methodology, and organizational needs and shape the team accordingly.
Make sound decisions in the absence of perfect information and help others navigate ambiguity with clarity and structure.