Clinical Data Scientist
Summary
Parse, interpret, and visualize clinical trial data to support study execution, collaborating with biostatistics and operations teams using programming tools.
Position Summary
The Clinical Data Scientist will support clinical development programs through data-driven insights, analysis, and high-impact visual communication. This role is responsible for parsing and interpreting clinical trial data, generating figures and presentations, and supporting data review activities to inform study execution and development strategy.
The individual will work closely with Biostatistics, Data Management, and Clinical Operations to ensure data quality, consistency, and usability. Success in this role requires strong programming and data visualization skills, along with the ability to translate complex clinical data into clear, actionable insights for both internal and external stakeholders.
Responsibilities
- Parse, review, and structure clinical trial data to support study interpretation, decision-making, and leadership updates.
- Develop high-quality graphs, tables, and figures using programming and analytics tools for analysis and presentation purposes.
- Act as a liaison between Biostatistics/Stat Programming, Data Management and Clinical Operations. Experience working with EDC data, Lab imaging data and external vendor data.
- Collaborate with Biostatistics, Data Management, and Clinical Operations to support data cleaning, reconciliation, and study tracking.
- Perform ongoing data review to assess safety, efficacy, and operational metrics.
- Create clear, visually compelling slide decks and materials for senior leadership, study teams, investigators, and governance forums.
- Contribute to clinical study documentation, including protocols, statistical analysis plan (SAP) support materials, clinical study reports (CSRs), and interim analyses.
- Interpret emerging study data and communicate findings in a concise, structured, and clinically relevant manner.
- Maintain proficiency in programming, reporting, and visualization tools to support evolving data needs.
- Identify trends, anomalies, and data quality issues, and proactively escalate findings to relevant stakeholders.