Product Owner for ML Interface Solutions

Role Summary

Product Owner for ML Interface Solutions based in Cambridge, MA. Bridges AI/ML models and scientists by delivering intuitive front-end interfaces that transform ML predictions into actionable insights for Large Molecule Research (LMR) scientists. Owns the product roadmap and backlog, partners with data scientists, ML engineers, and bench scientists to translate complex capabilities into user-centered solutions that accelerate biologics discovery.

Responsibilities

• 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
• 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
• 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
• 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

Education

• Bachelor Degree required with significant industry experience in computational biology (antibody discovery, protein design, large molecules or related topic). MS or PhD preferred.

Experience

• 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

Skills

• 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