Senior Clinical Data Scientist

Overview

The Senior Clinical Data Scientist is responsible for applying advanced analytics, machine learning, AI-driven methodologies, and clinical expertise to generate actionable insights, predictive capabilities, and scalable data solutions. This role partners with cross-functional stakeholders to translate complex clinical and operational problems into analytical strategies, evaluating data risk and delivering insights that inform decision making related to patient safety, study execution, data integrity, and portfolio-level decisions.

Responsibilities

• Develop and deploy advanced analytical models, including machine learning, predictive modeling, forecasting, and anomaly detection.
• Apply AI/ML techniques to solve complex business problems and identify patterns, risks, and opportunities within large, multi-source datasets.
• Design, build, and maintain scalable analytical solutions and reusable data science frameworks.
• Translate analytical findings into clear, accurate, and actionable insights tailored for technical and non-technical stakeholders.
• Partner with cross-functional teams to ensure insights are actionable and aligned to business priorities.
• Contribute to the development of KPIs, metrics, and performance measurement frameworks in collaboration with cross-functional stakeholders.
• Mentor peers and contribute to building data science and AI/ML capabilities across the organization.
• Apply advanced analytics to patient-level, site-level, and study-level clinical data, processes, and documents to support study design and strategy, execution, oversight, and portfolio insights including potential claims.
• Integrate and analyze data across clinical systems (e.g., EDC, CTMS, eTMF, safety systems, external data sources) to provide a holistic view of site and study performance and data reliability.
• Evaluate data quality, data risk, and data completeness in the context of clinical trial conduct, identifying potential impacts to patient safety and endpoint integrity.
• Ensure analytical approaches align with regulatory authority expectations for electronic records (e.g., data integrity, traceability, reproducibility) and the use of AI in generating outputs.
• Support inspection readiness through well-documented processes and auditable analytical outputs.

Qualifications

• Education: Bachelor’s degree (Masters or PhD preferred) in Data Science, Biostatistics, Statistics, Computer Science, Biomedical Engineering, or a related quantitative discipline; or equivalent combination of education and experience.
• Experience: Minimum 5–8 years of relevant experience in data science, advanced analytics, or AI/ML.
• Technical Skills: Strong proficiency in analytical programming languages and tools (e.g., Python, R, SQL). Experience developing analytical models and data visualization tools (e.g., Power BI). Experience developing and deploying machine learning models. Familiarity with data engineering concepts and working with large datasets.
• Competencies: Strong problem-solving and analytical thinking skills. Ability to work in ambiguous environments and define analytical approaches. Strong communication skills with the ability to translate technical findings into business insights. Collaborative mindset with experience working in cross-functional teams. Comfortable working with ambiguous questions and incomplete data while clearly articulating assumptions and limitations.
• Domain Knowledge: Working knowledge of clinical research systems, and data and documentation management structures (e.g., EDC, CTMS, eTMF, CDISC, CDASH). Understanding of clinical study execution, oversight, and operational workflows within a regulated environment.