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Experience: 5+ years of experience in product ownership and translating scientific business requirements into technical specifications, Proven track record of…

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Product Owner for AI & Molecular Modeling

Sanofi -
Barcelona, Barcelona provincia

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Detalles del empleo

Cualificaciones

Jira
Doctorado
Máster
Grado
Machine learning
Deep learning
Usabilidad
UX
Scrum
APIs
UI
Requisitos comerciales
Inteligencia artificial
Figma
Liderazgo
Python

Descripción completa del empleo

Job title: Product Owner for AI & Molecular Modeling

Location: Barcelona, Spain

About the job

We are an innovative global healthcare company with one purpose: to chase the miracles of science to improve people’s lives. We’re also a company where you can flourish and grow your career, with countless opportunities to explore, make connections with people, and stretch the limits of what you thought was possible. Ready to get started?

Join our Digital Team as Product Owner for AI & Molecular Modeling and you can help make it happen. Our mission is to pioneer next-generation digital products that reshape how R&D discovers, designs, and develops new medicines. We harness cutting-edge AI, machine learning, and computational modeling to build transformative in-silico solutions—empowering scientists with predictive insights, streamlining complex workflows, and turning data into decisive action. Your job? You will play a pivotal role in bridging the gap between powerful computational predictive models and the medicinal chemists and computational chemists who rely on them. This position leads to the vision and execution of AI/ML-driven digital products leveraging chemoinformatics, molecular modeling, and Computer-Aided Drug Design (CADD) methodologies, with a focus on small molecule drug design.

Main responsibilities:

Lead agile and iterative product development in cross-functional pods with data scientists, ML engineers, UX/UI designers, and data engineers
Define detailed user stories, acceptance criteria, and success metrics grounded in scientific workflows
Coordinate with MLOps and data engineering teams to validate technical requirements and architecture decisions
Define, establish, and monitor KPIs and success metrics to demonstrate product value and return on investment
Build and present executive-level reports and dashboards communicating product Progress, adoption metrics, and value delivered
Translate complex technical and scientific outcomes into clear business impact narratives for senior leadership
Coordinate and align cross-functional agile teams including data scientists, ML engineers, computational chemists, and UX designers
Conduct user research with molecular design scientists to understand workflows, data needs, and predictive modeling requirements
Lead usability testing sessions and gather continuous feedback from scientist users
Define, establish, and monitor KPIs and success metrics to demonstrate product value and return on investment
Build and present executive-level reports and dashboards communicating product progress, adoption metrics, and value delivered
Translate complex technical and scientific outcomes into clear business impact narratives for senior leadership
Conduct regular product reviews with stakeholders to assess performance against strategic objectives
Proactively identify and communicate risks, trade-offs, and mitigation strategies to leadership
Serve as the primary translator between AI/ML and molecular modeling technical capabilities and scientific user needs
Maintain deep knowledge of CADD methodologies (molecular docking, molecular dynamics, QSAR, free energy perturbation, pharmacophore modeling) to make informed product decisions
Stay current on emerging AI/ML approaches in drug discovery, including generative molecular design, structure-based AI, and multi-parameter optimization
Collaborate with data engineers to ensure robust data pipelines and APIs support seamless user experiences
Ensure proper integration with existing molecular design tools, cheminformatics platforms, and scientific databases
Defining and tracking product KPIs and communicating results to executive stakeholders

About you

Experience: 5+ years of experience in product ownership and translating scientific business requirements into technical specifications, Proven track record of leading cross-functional agile teams in a scientific or R&D environment, Hands-on experience with AI/ML, molecular modeling, or CADD methodologies in a drug discovery context, Experience defining and tracking product KPIs and communicating results to executive stakeholders
Skills:
- Knowledge of CADD and molecular modeling methods: molecular docking, molecular dynamics, QSAR, free energy perturbation, pharmacophore modeling, and structure-based drug design
- Proven experience with AI/ML applications in drug discovery and small molecule design
- Strong command of agile product development methodologies (Scrum, Kanban) and cross-functional team leadership
- Ability to define, measure, and communicate product impact through KPIs and executive-level reporting
- Strong ability to translate complex computational concepts into intuitive user experiences
- Proficiency with product management tools (JIRA, Confluence, Figma, Miro, or equivalent)
- Excellent communication and stakeholder management skills across scientific and business audiences
- Change management skills to drive adoption of new digital tools and workflows
Education: Bachelor's degree in Life Sciences, Computational Chemistry, Molecular Modeling, Cheminformatics, Biochemistry, or a related field with significant industry experience, Master's or PhD degree strongly preferred
Languages: Excellent English communication and writing

What Makes You Stand Out:

Hands-on experience with python-based libraries for molecular manipulation (RDKit, OpenBabel, DeepChem) and vizualization (Pymol, ChemPy, Streamlit, MDAnalysis)
Experience with generative AI or deep learning approaches for molecular design (e.g., graph neural networks, diffusion models, transformer-based molecular generation)
Track record of driving measurable improvements in user adoption, scientific productivity, or drug design cycle time
Knowledge of regulatory considerations for AI/ML tools in drug discovery
Experience with design thinking methodologies and facilitation of co-creation workshops
Background combining wet-lab or medicinal chemistry experience with computational/data science expertise
Curiosity and continuous learning mindset regarding emerging technologies in AI/ML and scientific computing

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