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Statistical Programmer

Pubblicato il 03-09-2026 - Chiesi in Italia

Statistical Programmer - Chiesi - Parma, Emilia-Romagna, IT About us: Chiesi is an international research-focused biopharmaceutical group with 90 years' experience, operating in more than 30 countries, with its Headquarter in Parma, Italy.
More than 8,000 employees across the group are united by a singular purpose: promoting a healthier world for our people, patients, and the planet.
This is what drives us as we research, develop, and market innovative drugs across our main therapeutic areas.
Discover more here. At Chiesi we are thinking generations ahead, driving sustainable innovation with purpose.
This fosters a culture of reliability, transparency, and ethical behaviour at every level.
As a Vantaggi Corporation and a certified B Corp, we have embedded sustainability in our bylaws and continuously measure our impact. Diversity and inclusion are at the heart of who we are.
We believe our differences make us stronger.
We are a vibrant ecosystem of passionate, talented individuals united by strong values,



each bringing unique perspectives that help us to continuously challenge the status quo for the better.
Chiesi Global Rare Diseases Chiesi Global Rare Diseases is a Chiesi Group's business unit established in February 2020 and focused on research, development and commercialization of treatments and patient support services for rare and ultra-rare disorders, many of whom have limited or no treatments available. This unit is based in Boston, Massachusetts, with a strong synergy with the headquarters in Parma.
Rare unit has a focus on research and product development for lysosomal storage diseases, rare hematology, ophthalmology disorders and rare immunodeficiencies.
The unit is also a dedicated partner with global leaders in scientific research, patient advocacy and care.
What you will do
- Establish the SAS programming needs for a project and develop a work-plan to deliver SAS outputs to meet project deliverables.
- Responsible for the production of quality data outp

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