Head of R&D Data Elaboration

Overview

Strategic leader driving the digital transformation of R&D through advanced data elaboration, modelling and visualization. You will grow and mentor a team, translate research needs into data solutions, and shape the data-centric roadmap across R&D. Your work turns complex data into actionable insights, leveraging HPC and external partnerships. This role strengthens Chiesi's R&D capabilities and global collaboration.

Retribuzione / Benefits

• remote work opportunities
• flexible working approach
• relocation support
• tax assistance for foreign colleagues
• people-care services
• continuous training and development

Responsabilità

• Lead and grow a high-performing data elaboration and visualization team
• translate stakeholder needs in R&D into data solutions aligned with research goals
• drive the R&D digital transformation by implementing the data roadmap
• oversee the lifecycle of data models and visualizations from design to deployment
• contribute to budget planning for data elaboration initiatives
• develop advanced analytical models to enable data-driven decisions
• support the R&D data platform, including HPC-enabled analytics
• establish quality standards for data elaboration and modeling
• collaborate with COIC to enhance data capabilities
• consolidate the digital network with internal and external partners
• support cross-functional corporate data initiatives impacting R&D
• forge and manage strategic partnerships with external orgs and academia
• communicate findings to R&D leadership through clear reports
• stay updated on latest data visualization and analytics trends
• champion adoption of new technologies in R&D
• enhance Chiesi's reputation as leader in analytics and tech
• participate in AI governance as part of the R&D Vertical Committee and R&D Squad

Requisiti fondamentali

• PhD or Master's in relevant field
• minimum 5+ years leading data elaboration/computation modelling in pharma/biotech/medtech
• strong communication to both technical and non-technical audiences
• team-oriented and cross-functional collaboration
• attention to detail and scientific rigor
• data visualization tools (Tableau, Power BI)
• programming languages (Python, R, JavaScript)
• data manipulation (SQL)