Product Owner for ML Interface Solutions

Role Summary

The Product Owner for ML Interface Solutions bridges AI/ML models and scientists, owning the roadmap and backlog for intuitive front-end interfaces that translate ML outputs into actionable insights for Large Molecule Research. You will lead agile efforts to deliver user-centered interfaces, collaborate with data scientists, ML engineers, and bench scientists, and drive adoption through clear visualization and workflow integration in 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 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

Qualifications

• 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
• Experience collaborating with data scientists and developers in a scientific environment
• Experience delivering digital products with intuitive, user-friendly interfaces and effective data visualizations
• Required: Solid understanding of biologics discovery workflows and antibody/protein engineering challenges
• Required: Strong ability to translate complex concepts into intuitive user experiences and compelling visualizations
• Required: Experience with agile product development methodologies (Scrum, Kanban) and cross-functional teams
• Required: Familiarity with ML/AI applications in drug discovery, with ability to understand and interpret model outputs
• Required: Change management skills to drive adoption of new tools and workflows
• Required: Experience with product management, ideation and design platforms tools such as Figma, Miro, JIRA, or similar
• Required: Ability to balance competing priorities and make data-driven decisions about feature prioritization
• Preferred: Curiosity about emerging technologies in AI/ML and scientific computing

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 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

Education

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