Product Owner for AI & Molecular Modeling

Job Title Product Owner for AI & Molecular Modeling Location Cambridge, MA About the Job At Sanofi, we chase the miracles of science to improve people’s lives. The Digital In‑Silico Research team is a key innovation engine within Digital R&D, dedicated to pioneering 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. 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; ensure delivered products are scientifically rigorous, scalable, and accessible to non‑computational scientists. 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. Conduct regular product reviews with stakeholders to assess performance against strategic objectives; proactively identify and communicate risks, trade‑offs, and mitigation strategies to leadership. Coordinate and align cross‑functional agile teams, build partnerships with R&D scientists, and collaborate with the Product Line Owner on portfolio strategy, prioritization, and resource allocation. Conduct user research with molecular design scientists to understand workflows, data needs, and predictive modeling requirements; design intuitive interfaces and visualizations that make AI/ML predictions accessible, interpretable, and actionable. Lead usability testing sessions, gather continuous feedback, and drive product adoption through targeted training, documentation, and change management initiatives. Serve as the primary translator between AI/ML and molecular modeling technical capabilities and scientific user needs; maintain deep knowledge of CADD methodologies and stay current on emerging AI/ML approaches in drug discovery. Qualifications Education: Bachelor’s degree in Life Sciences, Computational Chemistry, Molecular Modeling, Cheminformatics, Biochemistry, Medicinal Chemistry or a related field; Master’s or PhD is a plus. 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. Demonstrated experience delivering digital products with measurable scientific or operational impact. 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. Strong command of agile product development methodologies (Scrum, Kanban) and cross‑functional team leadership. Excellent communication and stakeholder management skills across scientific and business audiences; change management skills to drive adoption of new digital tools and workflows. Proficiency with product management tools (JIRA, Confluence, Figma, Miro or equivalent). Skills Knowledge of CADD and molecular modeling methods such as 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. 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. Experience designing intuitive interfaces and visualizations for AI/ML predictions. Familiarity with Python‑based libraries for molecular manipulation and visualization (RDKit, OpenBabel, DeepChem, PyMOL, ChemPy, Streamlit, MDAnalysis). Experience with generative AI or deep learning approaches for molecular design (graph neural networks, diffusion models, transformer‑based molecular generation). Knowledge of regulatory considerations for AI/ML tools in drug discovery. Experience with design thinking methodologies and facilitation of co‑creation workshops. Curiosity and continuous learning mindset regarding emerging technologies in AI/ML and scientific computing. EEO Statement Sanofi Inc. and its U.S. affiliates are Equal Opportunity and Affidavit Action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, age, ancestry, nationality, marital status, sex, gender, gender identity or expression, sexual orientation, disability, veteran or military status, domestic violence victim status, genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law. #J-18808-Ljbffr Sanofi