• Lead or support data management study activities, CRO oversight, and driving deliverable timelines. • Strong knowledge of EDC builds. • Represents data management function on the Clinical Sub-team ensuring aligned expectations between the CRO and client for all data related deliverables, especially in support of key decision points and regulatory submissions. • Contributes influential leadership in collaboration with other client Stakeholders to ensure established milestones and deliverables are met with the highest degree of quality. • Partners with appropriate stakeholders and CRO partners to mitigate and resolve risks. • Provides input to functional governance with client strategic suppliers. Partners with appropriate stakeholders to resolves issues escalated from the vendor and/or cross-functional teams. • Participates in preparing function for submission readiness and may represent function in a formal inspection or audit. • Participates and represents function in formal inspections and audits as requested. • Ensure archival and inspection readiness of all Data Management Trial Master File (TMF) documents. • Ensures achievement of major data management deliverables and milestones in coordination with other functions including the Therapeutic Area Units, Clinical Operations, Statistical Programming and Statistics. • Responsible for the planning and management of external Data Management budgets and timelines to ensure accuracy, understand trends in variances and support continuous improvement in forecasting. • Acts as a process expert for operational and oversight models. • Maintains SOPs, process maps and templates and timelines to support functions operational and oversight models. • May prepare metrics to support the function’s KPIs. • Represents function in external professional initiatives and organizations such as SCDM, CDISC, DIA, etc. to identify industry best practice and increase the visibility of client. • Contributes to functional Continuous Improvement initiatives, providing strategic direction and identifying key deliverables that meet timelines, budget, and are in alignment with company, departmental or functional requirements. • Ensure compliance with own Learning Curricula, corporate and/or GXP requirements. • Works cross-functionally to ensure the quality of the data in each database and on time delivery, as well as quality of other data management deliverables.

• Build, validate, and deploy statistical and AI models to solve complex business problems. • Design and refine predictive features, ensuring data quality and model interpretability. • Leverage Azure Machine Learning and Microsoft’s suite of data services to scale model training and deployment. • Apply statistical rigor to analyze data trends and communicate findings to non-technical stakeholders.

• Analyzes business requirements and determines a suitable solution autonomously, evaluating if an ML-based solution is feasible • Good understanding of business requirements • Develops and fine-tunes models through reproducible experiments • Builds ML solutions incorporating software engineering quality standards (SDLC) and data engineering best practices • Participates in the technical design of features with guidance • Understands and optimizes and monitors model performances • Prioritizes tasks with autonomy based on requirements and proper context.

• Lead the data science lifecycle, from discovery and exploration to delivery and customer hand-off • Translate ambiguous business problems into clear problem statements and deliver working, production-ready solutions • Partner with customers and internal stakeholders to co-design use cases, define success criteria, and ensure models are adopted and successful in production • Build quick, iterative prototypes to test ideas, and work cross-functionally to deploy validated solutions into production environments • Own and improve pipelines for data integration, validation, and monitoring, ensuring long-term stability and performance • Analyze large-scale structured and unstructured datasets to extract insights and power model development • Apply machine learning, NLP, graph analytics, and other advanced techniques to solve challenging problems such as semantic search, or anomaly detection • Create and maintain internal data science tooling to streamline experimentation, testing, and delivery • Support internal teams and customers with issue resolution, ensuring data science solutions continue to deliver value post-deployment • Follow and advocate for best practices in MLOps, DataOps, and clean code to ensure reproducibility, maintainability, and scalability