Who we are looking for

This is what you will do

The Data Scientist is responsible for designing, developing, and deploying advanced Data Science, Artificial Intelligence (AI), Machine Learning (ML), and Generative AI solutions to support Clinical Development across the drug development lifecycle.
The role combines strong expertise in data science with a solid understanding of clinical research and drug development. Working with clinical trial data, real-world data, digital health technologies, Data Scientist will generate actionable insights that improve decision-making, increase operational efficiency, accelerate evidence generation, and enhance patient-centric approaches to clinical research.
As part of a multidisciplinary environment, the individual will collaborate closely with Global Clinical Development teams, and Digital teams to identify opportunities where data science can create measurable impact throughout the clinical development continuum.

You will be responsible for

Data Science and AI Solutions for Clinical Development
• Design, develop, validate, and deploy advanced analytics, machine learning, artificial intelligence, and Generative AI solutions addressing scientific, clinical, and operational challenges across Clinical Development and Clinical Operations.
• Apply statistical modelling, predictive analytics, AI, and data science methodologies to generate insights from diverse healthcare and clinical data sources.
• Transform complex clinical and operational data into actionable recommendations that support evidence-based decision-making.
• Leverage clinical trial data, real-world data, digital health data to improve the quality, efficiency,



and effectiveness of clinical development activities.
• Evaluate emerging technologies, data sources, and analytical methodologies that can enhance clinical research and patient outcomes.
• Ensure developed solutions are scientifically robust, reproducible, scalable, and compliant with regulatory, privacy, and governance requirements.
• Contribute to the definition of data science best practices, standards, and analytical frameworks within the organization.
Examples of potential application areas include:
• Clinical trial design and protocol optimization.
• Real World Data (RWD) and Real World Evidence (RWE) analytics.
• Synthetic control arms and virtual patient cohorts.
• Predictive analytics for trial performance assessment and monitoring.
• Risk identification and study performance forecast.
• Development and validation of digital biomarkers.
• AI and Generative AI applications supporting clinical research, risk assessment and decision-making.
Strategic Collaboration and Stakeholder Engagement
• Partner closely with Global Clinical Development and Digital teams.
• Translate scientific, clinical, operational, and business challenges into robust analytical solutions.




• Facilitate the adoption and implementation of advanced analytics and AI solutions across clinical functions.
• Present complex analytical findings clearly and effectively to both technical and non-technical stakeholders.
• Support strategic decisions through data-driven insights and evidence generation.
• Act as a trusted advisor on data science, artificial intelligence, and advanced analytics opportunities within Clinical Development.
Innovation and External Collaboration
• Monitor scientific and technological advancements in AI, machine learning, digital health, clinical data science, and related fields.
• Collaborate with academic institutions, technology providers, CROs, healthcare organizations, and industry consortia.
• Contribute to innovation initiatives aimed at transforming clinical development through data-driven methodologies.
• Evaluate external technologies, platforms, and partnerships that can accelerate capability development.
• Participate in scientific publications, conference presentations, and external collaborations when appropriate.
• Foster a culture of innovation, continuous learning, and scientific excellence.

You will need to have

Technical Skills
• Strong knowledge of machine learning, artificial intelligence, predictive analytics, and statistical modelling techniques.
• Proficiency in programming languages commonly used in data science (e.g., Python, R).
• Experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
• Experience developing and deploying machine learning models in production environments.

📌 Senior Data Scientist (Parma)
🏢 Chiesi Group
📍 Parma