Computational Science Lead

Description

Summary: Lead the development of modeling and data frameworks to enable smarter clinical trial design, real-time operational insights, and scalable analytics. Highlights: 1. Lead development of end-to-end clinical AI workflows. 2. Drive modeling strategy, ensuring scientific rigor. 3. Mentor and guide junior scientists. **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 \#LI\-Hybrid \#BarcelonaHub \#SanofiHubs null