Sanofi-posted 3 days ago
Full-time • Mid Level
Onsite • Cambridge, MA
5,001-10,000 employees

At Sanofi, we chase the miracles of science to improve people’s lives. We believe our cutting-edge science and manufacturing, fueled by data and digital technologies, have the potential to transform the practice of medicine, turning the impossible into possible for millions of people. As one of the leading investors in life sciences, manufacturing and research and development, we focus on delivering new and better ways to address unmet medical needs. Our products empower self-care, prevent and treat diseases, and help people live better. Digital & Data is at the heart of Sanofi: our ambition is to be the leading digital healthcare platform to develop & deliver medicine faster, enable healthcare professionals to improve treatments and help patients improve their health. Our scale, strong connections within health ecosystems across leveraging the world, and ability to leverage Sanofi’s capabilities make us the best place to push the boundaries of medicine through technology. 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. As the Product Owner for ML Interface Solutions , you will play a critical role in bridging the gap between powerful AI/ML models and the scientists who use them. Reporting to the In Silico Molecule Design Product Line Owner, you will create intuitive, user-friendly front-end interfaces that make complex ML tool outputs accessible and actionable for Large Molecule Research (LMR) scientists. Your mission is to transform computational predictions into compelling visualizations and workflows that seamlessly integrate into biologics discovery processes. This role sits at the intersection of cutting-edge AI/ML technology and user-centered design, where you will have direct impact on how AI integrates to research workflow to drive insights. You will partner closely with data scientists, ML engineers, and scientists to understand both the technical capabilities of our models and the practical needs of bench scientists. Through these collaborations, you will translate complex technical capabilities into user-centered solutions that enable scientists to make faster, more informed decisions in antibody discovery, protein engineering, and biologics optimization. You will own the product roadmap and backlog for ML interface solutions, working in agile pods to deliver iterative improvements that enhance user experience and accelerate adoption. A critical aspect of this role involves continuously evaluating research scientists' activities to identify bottlenecks and inefficiencies that can be addressed through innovative digital solutions. You will balance scientific accuracy with intuitive design principles, ensuring that interfaces are accessible and usable by non-computational scientists who need to leverage AI insights confidently in their daily work. Success in this role includes creating AI solutions that become essential tools in scientific research workflows, transforming how they leverage computational insights to drive scientific discovery and ultimately accelerating Sanofi's biologics research pipeline. About Sanofi: We’re an R&D-driven, AI-powered biopharma company committed to improving people’s lives and delivering compelling growth. Our deep understanding of the immune system – and innovative pipeline – enables us to invent medicines and vaccines that treat and protect millions of people around the world. Together, we chase the miracles of science to improve people’s lives.

  • Product Development & Delivery: Own and prioritize the product roadmap and backlog for ML interface solutions serving LMR scientists Partner with data scientists and ML engineers to deeply understand model outputs, capabilities, and limitations to effectively support scientists on decision making Design and deliver intuitive front-end interfaces that make ML predictions accessible, interpretable, and actionable for bench scientists Define detailed user stories, acceptance criteria, and success metrics for interface features based on scientific workflows Lead agile development with UX/UI designers, front-end developers, and data engineers to deliver iterative improvements Establish frequent touchpoints with software development and MLOps teams to validate technical requirements and architectural elements necessary for optimal performance of ML solutions. Ensure interfaces properly communicate model uncertainty, confidence levels, and appropriate scientific context Balance new feature development with technical debt and user feedback
  • User Experience & Adoption: Conduct comprehensive user research with LMR scientists to understand their workflows, pain points, data and interface needs Champion user-centered design principles throughout the product lifecycle Lead usability testing sessions and gather continuous feedback from scientist users Create compelling data visualizations that make complex ML predictions interpretable and scientifically meaningful Drive product adoption through targeted training, comprehensive documentation, and effective change management Ensure interfaces are scientifically rigorous while remaining intuitive for users with varying computational backgrounds Monitor usage metrics and user satisfaction to continuously guide product improvements Develop onboarding materials and user guides that accelerate scientist proficiency with ML tools
  • Stakeholder Collaboration: Build strong partnerships with LMR scientists to deeply understand biologics discovery workflows and research challenges Collaborate closely with ML/AI teams to stay current on model capabilities, new algorithms, and technical constraints Work with UX/UI designers to create visually compelling and scientifically accurate interfaces Coordinate with the Product Line Owner on portfolio strategy, prioritization, and resource allocation Communicate product progress, value delivered, and adoption metrics to stakeholders and leadership Facilitate co-creation workshops and requirements gathering sessions Manage expectations and negotiate trade-offs between user desires and technical feasibility
  • Technical & Scientific Bridge: Serve as the translator between technical ML capabilities and scientific user needs, ensuring both perspectives are represented Ensure ML model outputs are presented with appropriate scientific context, limitations, and uncertainty quantification Collaborate with data engineers to ensure robust data pipelines and APIs support seamless user experiences Advocate for backend improvements (API design, data structures, model outputs) that enable better front-end experiences Stay current on best practices in scientific data visualization and interface design for computational biology Understand the technical architecture sufficiently to make informed product decisions and identify integration opportunities Work with scientific informatics teams to ensure proper integration with existing LMR tools and databases
  • Bachelor Degree required with significant industry experience in computational biology (antibody discovery, protein design, large molecules or related topic). MS or PhD preferred.
  • 5+ years of experience in product management and translating business requirements to technical specifications.
  • Proven track record in collaborating with data scientists and developers in a scientific environment.
  • Experience delivering digital products with intuitive, user-friendly interfaces and effective data visualizations
  • Solid understanding of biologics discovery workflows and antibody/protein engineering challenges
  • Strong ability to translate complex concepts into intuitive user experiences and compelling visualizations
  • Solid experience with agile product development methodologies (Scrum, Kanban) and working in cross-functional teams
  • Familiarity with ML/AI applications in drug discovery, with ability to understand and interpret model outputs
  • Change management skills to drive adoption of new tools and workflows
  • Experience with product management, ideation and design platforms tools such as Figma, Miro, JIRA, or similar
  • Ability to balance competing priorities and make data-driven decisions about feature prioritization
  • Curiosity about emerging technologies in AI/ML and scientific computing
  • Background in both wet-lab biology and computational/data science
  • Knowledge of regulatory considerations for AI/ML tools in drug discovery
  • Understanding of API integration, data pipeline concepts, and how backend architecture impacts user experience
  • Track record of driving measurable improvements in user adoption and satisfaction
  • Portfolio demonstrating successful scientific interface or visualization projects
  • Experience with design thinking methodologies and facilitation
  • Bring the miracles of science to life alongside a supportive, future-focused team.
  • Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
  • Enjoy a thoughtful, well-crafted rewards package that recognizes your contribution and amplifies your impact.
  • Take good care of yourself and your family, with a wide range of health and wellbeing benefits including high-quality healthcare, prevention and wellness programs and at least 14 weeks’ gender-neutral parental leave.
  • Executive sponsorship and governance, with newly appointed CDO & leadership team
  • Digital & data culture in place with agile ways of working and a strong ecosystem (Sanofi Ventures, BD Partnerships)
  • Unique diversity of medical & technical challenges, with mobility opportunities
  • Opportunities to learn and grow in various aspects of computational chemistry/biology and drug development.
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