Experteer Overview
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 / Ventajas
• 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 applications

Responsabilidades
• 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 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