Business Services & Consulting • all cities, PA 39
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.
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.
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.
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.
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.
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.
