Computational Science Lead

• Location: Barcelona

About the Job

At Sanofi, we chase the miracles of science to improve people’s lives. Within Digital R&D, the Integrative Clinical Data (ICD) team builds AI-powered products that transform how clinical trials are designed, executed, and optimized.

This role sits at the intersection of trial design, operational analytics, and AI-driven decision systems. You will lead the development of modeling and data frameworks that enable smarter trial design, real-time operational insights, and scalable analytics across clinical programs.

You will work across end-to-end data flows - from raw clinical and operational data to production-grade AI models and agentic systems. Your work will span in-silico trial prediction, patient representation learning, disease progression modeling, clinical foundation models, with extensions into trial enrollment, site intelligence, probability of technical and regulatory success (PTRS) modeling, and end-to-end trial optimization with agents.

As a Lead Computational Scientist, you will operate as a technical owner across initiatives, driving modeling strategy, ensuring scientific rigor, and enabling deployment of decision-grade insights into our Drug Development products.

Key Responsibilities

• Lead development of end-to-end clinical AI workflows, spanning data ingestion, curation, feature engineering, modeling, validation, and deployment across clinical trial design, execution, and optimization use cases
• Design, own and implement advanced modeling approaches for in-silico trial prediction, patient representation learning, disease progression modeling and other development AI use cases – with an evaluation first mindset
• Translate clinical development questions into scalable computational solutions, partnering with clinical, biostatistics, and product teams to define appropriate modeling strategies and success criteria
• Drive integration of models into production systems and decision workflows, collaborating with engineering teams to ensure robustness, scalability, and usability
• Define and implement validation frameworks, including statistical evaluation, temporal validation, and alignment to clinical and regulatory expectations
• Communicate insights through clear narratives, visualizations, and decision frameworks, enabling adoption by clinical teams, study leads, and senior leadership
• Mentor and guide junior scientists, providing direction on modeling approaches, study design, and best practices in machine learning and data science
• Contribute to scientific leadership and external impact, including publications, conference submissions (e.g., ML4H, NeurIPS, AMIA), and cross-industry/academia collaborations
• Identify and drive innovation opportunities across clinical AI, multimodal modeling, and agent-based systems for trial operations
• Stay current with advancements in machine learning, generative AI, and clinical data science, and help translate these into practical applications across the organization

About You

Qualifications

• 5+ years of experience in data science, machine learning, computational biology, or related quantitative fields, with demonstrated ownership of end-to-end analytical or modeling workflows
• Advanced degree (Master’s or PhD) in a quantitative discipline (e.g., computer science, statistics, engineering, computational biology, applied mathematics)
• Strong programming experience in Python (preferred), with deep familiarity in scientific computing and machine learning frameworks (e.g., PyTorch, scikit-learn)
• Experience applying software engineering best practices to data and ML systems, including version control, testing, modular code design, and reproducible workflows
• Proven experience developing and deploying machine learning models on complex biomedical or clinical datasets (e.g., EHR, clinical trials, real-world data, imaging, multimodal data)
• Experience developing or applying agent-based or AI-driven decision systems, integrating machine learning models, data pipelines, and reasoning workflows to support complex tasks (e.g., clinical trial operations, monitoring, or optimization)
• Strong understanding of model validation, experimental design, and performance evaluation in real-world or clinical settings
• Experience working with data pipelines and large-scale datasets, including preprocessing, feature engineering, and reproducible workflows
• Ability to translate ambiguous business or clinical problems into structured analytical approaches
• Strong communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders
• Preference for a track record of publications or contributions to machine learning conferences (e.g., NeurIPS, ICML, ICLR, ML4H) or related journals
• Preference for experience working with cloud platforms and data infrastructure (e.g., AWS, Snowflake, Spark/PySpark)

Why Choose Us?

• Bring the miracles of science to life alongside a supportive, future-focused team
• Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally
• Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact
• Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high-quality healthcare, prevention and wellness programs and at least 14 weeks’ gender-neutral parental leave

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