Evidence Generation Manager

17 ott - Milano
Novartis Italia

Summary At Novartis, we are committed to reimagining medicine to improve and extend people's lives. As an Evidence Generation Manager, you will play a key role in planning, coordinating, and overseeing country-level evidence generation activities, including observational research, local clinical studies, and real‑world evidence (RWE) programs. The role is accountable for defining and aligning evidence generation priorities with asset strategies and integrated evidence plans, while ensuring the highest standards of scientific quality, compliance, and operational excellence. This position sits within the Medical Governance, Evidence & Operations organization.
At Novartis, we are committed to reimagining medicine to improve and extend people's lives. As an Evidence Generation Manager, you will play a key role in planning, coordinating, and overseeing country-level evidence generation activities, including observational research, local clinical studies, and real‑world evidence (RWE) programs.



The role is accountable for defining and aligning evidence generation priorities with asset strategies and integrated evidence plans, while ensuring the highest standards of scientific quality, compliance, and operational excellence. This position sits within the Medical Governance, Evidence & Operations organization.
This is a 12-month Fixed-Term contract.
About The Role Key Responsibilities Evidence Generation Planning & Oversight Coordinate country-level evidence generation activities, including observational studies, real‑world evidence programs, and local non‑interventional studies.
Ensure evidence generation activities are aligned with approved Integrated Evidence Plans (IEPs), asset strategies, and regional/global guidance.
Partner with Medical Leads and Medical Managers to support the compliant communication and dissemination of locally generated evidence.
Provide scientific expertise on evidence generation methodologies, emerging technologies, and innovative approaches to

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