Product Owner for AI & Molecular Modeling

Job Summary

Product Owner for AI & Molecular Modeling located in Barcelona, Spain. The role leads the vision and execution of AI/ML‑driven digital products leveraging chemoinformatics, molecular modeling, and CADD methodologies to support medicinal and computational chemists.

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.
• Establish and monitor KPIs and success metrics to demonstrate product value and ROI.
• 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.
• 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.
• 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.

Qualifications

• 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, structure‑based drug design); experience with AI/ML applications in drug discovery and small molecule design; strong command of agile product development methodologies (Scrum, Kanban); 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 skills.

What Makes You Stand Out

• Hands‑on experience with python‑based libraries for molecular manipulation (RDKit, OpenBabel, DeepChem) and visualization (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.

EEO Statement

At Sanofi, we provide equal opportunities to all regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, ability or gender identity.

Salary and Benefits

Salary is determined based on demonstrated experience, skills, location, and other relevant factors. Employees may be eligible to participate in Company employee benefit programs.