• Drive signal detection and early alerting by applying new technologies to reduce false-positive rates and increase confidence in results through reproducible analysis approaches and controls. • Standardize signal review outputs (evidence summaries, trend context, case series characterizations) and ensure consistent governance across multiple data sources. • Maintain ongoing tracking and documentation of the signal lifecycle so that conclusions, decisions, and actions remain transparent and traceable. • Continuously improve the quality of pharmacovigilance data (completeness, consistency, de-duplication, coding, timeliness) and deliver "easy-to-digest" views (dashboards, scorecards, automated reports) to increase transparency of safety trends and data reliability. • Provide decision-ready insights to management via regular updates and ad-hoc escalations, with summaries focused on what has changed, why it matters, and what comes next; contribute to structured quarterly reviews for key products. • Partner with PV leadership and digital/automation teams to advance pharmacovigilance technologies and signal management capabilities — translate needs into clear requirements and acceptance criteria, support testing and implementation (training, feedback loops), and ensure continuity with minimal disruption.

• Design, develop, and deploy advanced data science solutions focused on time series modeling and optimization problems. • Analyze large and complex datasets to extract actionable insights that support business decision-making. • Build, validate, and maintain predictive and optimization models using Python and SQL. • Work closely with clients and internal stakeholders to understand requirements and present data-driven recommendations. • Collaborate with cross-functional teams, including data engineers and product teams, to ensure end-to-end solution delivery. • Contribute to best practices in modeling, experimentation, and analytical workflows. • Communicate findings clearly to both technical and non-technical audiences.

• Lead our data science initiatives, driving innovation and delivering data-driven insights • Lead the delivery of AI and data science projects, managing a team of 4–5 developers and data scientists • Design and implement AI solutions leveraging advanced techniques, including prompt engineering, agentic architectures, RAG, computer vision, predictive modeling, deep learning, and optimization • Collaborate with stakeholders to translate business requirements into scalable data science solutions • Ensure high-quality project execution, including architecture design, solution validation, deployment, and CI/CD management • Mentor and guide team members, fostering growth and knowledge sharing • Communicate complex technical concepts effectively to both technical and non-technical stakeholders • Maintain oversight of project timelines, resource allocation, and risk management

• Advance signal detection and early alerting by leveraging new technologies to reduce false positives and strengthen trust in results through reproducible analysis approaches and controls. • Standardize signal review outputs (evidence summaries, trend context, case-series characterizations) and ensure consistent governance across diverse data sources. • Maintain end-to-end signal tracking and lifecycle documentation so conclusions, decisions, and actions remain transparent and traceable. • Continuously improve pharmacovigilance data quality (completeness, consistency, de-duplication, coding, timeliness) and deliver “easy-to-digest” views (dashboards, scorecards, automated reports) to increase transparency into safety trends and data reliability. • Enable leadership-ready insight delivery through a regular cadence of updates and ad hoc escalations, with summaries focused on what changed, why it matters, and what’s next; contribute to structured quarterly reviews for key products. • Partner with PV leadership and digital/automation teams to evolve pharmacovigilance technology and signal management capabilities - translating needs into clear requirements and acceptance criteria, supporting testing and adoption (training, feedback loops), and ensuring continuity with minimal disruption.