Senior Clinical Data Scientist

Why Join Us We believe in taking care of our people so they can take care of others. As a global, family-owned company that has been improving patients’ lives since 1963, we’re committed to doing business with integrity and making a positive impact in the communities where we live and work. We’re guided by strong ethical standards and core values, and we believe the best ideas come from creating solutions together. Our company culture values every voice, brings together inclusive teams who collaborate well, and encourages learning and innovation. When you join Cook, you’re not just starting a job, you’re building a career with purpose, in a company that invests in you. What You Can Expect at Cook These are some ways we support our people across Cook: · Meaningful work & strong values: Contribute to improving patient care globally in a company known for high ethical standards and integrity. · Competitive rewards & security: Competitive, performance-related compensation and benefits that support your health and financial wellbeing. · Flexibility & time off: Paid time off, holidays, and hybrid or remote work options for many jobs, where possible. · Growth & development: Learning opportunities, educational assistance, and development programs to help you grow your skills and career. · Wellbeing & community: Health and wellness initiatives, social activities, and community engagement that help you feel connected and supported. Benefits and programs vary by country and job. Our recruiters can provide more information about the specific benefits and programs available in your location. Overview The Senior Clinical Data Scientist is an individual contributor responsible for applying advanced analytics, machine learning, AI-driven methodologies, and clinical expertise to generate actionable insights, predictive capabilities, and scalable data solutions. This Senior Clinical Data Scientist 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. The role is expected to demonstrate strong aptitude in applied AI, including identifying, designing, and implementing AI-enabled solutions in a responsible, ethical and governed manner. 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. Apply knowledge of Good Clinical Data Management Practices (GCDMP), and regulatory authority expectations to ensure data is not only complete, but credible and fit for decision-making. Support inspection readiness through well-documented processes and auditable analytical outputs. AI & Advanced Analytics Expectations Demonstrated experience applying machine learning and AI techniques in production or near-production environments Experience developing predictive models, classification models, or anomaly detection systems Familiarity with model lifecycle management, including validation, monitoring, and performance evaluation Ability to identify and implement AI-driven automation or decision-support solutions Awareness of ethical, regulatory, and governance considerations in AI/ML approaches, development and deployment within clinical research contexts, ensuring alignment with regulatory authority expectations, data privacy requirements, and patient safety considerations. Qualifications Educational Requirements: 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. Minimum 5–8 years of relevant experience in data science, advanced analytics, or AI/ML. Demonstrated experience working with complex, multi-source datasets in regulated environments. Experience working with patient-level clinical data, clinical operations data, CMS/claims data, or other regulated healthcare datasets strongly preferred. 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. Capable of acquiring and maintaining Cook Mastery Level of Artificial Intelligence competency, including the ability to assess feasibility and risk of AI use cases, design or coordinate workflow-level AI solutions, guide teams on responsible AI governance, mentor others, and contribute to organizational policies and ethical decision-making. Qualified candidates must be legally authorized in the United States. Cook does not intend to provide sponsorship for employment visa status’ for this employment position. Preferred 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.