Experteer Overview Asegúrese de leer detenidamente la información sobre esta posibilidad antes de presentar su candidatura. In this role you lead end-to-end clinical AI initiatives within Digital R&D;, guiding modeling strategy and deploying decision-grade insights for trial design and optimization. You will own end-to-end workflows from data ingestion to production models and agent-based systems, collaborating with clinical, biostatistics, and engineering teams. You’ll drive innovation in in-silico trials, patient representation, and disease progression modeling, shaping scalable analytics across clinical programs. The position combines scientific leadership with hands-on development in a fast, cross-functional environment.Compensaciones / Beneficios• Lead end-to-end clinical AI workflows: data ingestion, curation, feature engineering, modeling, validation, deployment• Develop advanced models for in-silico trial prediction, patient representation learning, and disease progression• Translate clinical questions into scalable computational solutions with clinical and product partners• Integrate models into production systems and decision workflows with robust,



scalable design• Define validation frameworks aligned with clinical and regulatory expectations• Communicate insights via narratives and visualizations for clinical teams and leadership• Mentor junior scientists on modeling approaches and best practices in ML and data science• Contribute to scientific leadership through publications and cross-industry collaborations• Identify opportunities across clinical AI, multimodal modeling, and agent-based systems• Stay current with ML advancements and translate them into practical applicationsResponsabilidades• 5+ years of experience in data science, ML, computational biology or related fields with end-to-end analytical ownership• Advanced degree in a quantitative discipline (Master or PhD)• Strong Python programming with PyTorch and scikit-learn• Experience applying software engineering practices to data and ML systems• Proven experience deploying ML models on biomedical/clinical datasets• Experience with agent-based or AI-driven decision systems in clinical contexts• Solid understanding of model validation xcskxlj and performance evaluation in real-world/clinical settings• Experience with data pipelines, feature engineering, and reproducible workflows• Ability to translate ambiguous problems into structured analytical approaches• Strong communication skills for technical and non-technical stakeholders• Publication track record or contributions to ML conferences (optional)• Experience with cloud platforms and data infrastructure (e.g., AWS, Snowflake, Spark/PySpark)Requisitos principales• health and wellbeing benefits• at least 14 weeks’ gender-neutral parental leave• competitive rewards package• career growth opportunities• hybrid work arrangement (#LI-Hybrid)• international opportunities

📌 Computational Science Lead (Barcelona)
🏢 Talent
📍 Barcelona