Product Owner - Machine Learning

Headquarters: United States Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions. Our verified identity platform and advanced image capture solutions are built on the latest advancements in biometric recognition, artificial intelligence, computer vision and machine learning, and trusted by over 7,500 organizations worldwide. We are headquartered in San Diego, California, with operations in the United Kingdom, Spain, France, Mexico, and the Netherlands. Visit us at www.miteksystems.com. At Mitek, we believe that teams are more resilient, effective, and innovative when they benefit from a wide range of ideas, lived experiences, and perspectives. The strength of our organization is deeply rooted in the people who power it.​​We know that a workforce reflecting the richness of our communities and customers helps us better serve their needs. These lived experiences influence our decisions, shape our products, services, and help us grow with intention. When it comes to talent, our goal is clear: to discover exceptional individuals and to ensure they discover us. We prioritize drive, skill, experience, and ambition in everything we do for our clients. We are Virtual 1st! Whether you choose to work remotely from your home office or in-person from one of Mitek’s offices, our practices, processes and tools are designed to enable your success. At Mitek, the Future of Work is about flexibility and preference wherever and whenever we are working. We are seeking a Product Owner to drive execution of machine learning based capabilities across biometric authentication and document verification. This role is deeply embedded with machine learning, engineering, and fraud teams, ensuring initiatives are clearly defined, well scoped, and delivered into production with measurable impact. This is a hands-on delivery role, not a strategy ownership position.  The ideal candidate has direct experience building and operating ML driven products in biometric and document environments, understands how these systems behave in production, and is comfortable challenging assumptions, proposing alternatives, and pushing back when needed to ensure outcomes align with fraud risk, customer needs, and operational realities.What You Will Do (Essential Responsibilities): ML Feature and Capability OwnershipOwn and manage the backlog for ML-driven biometric and document verification capabilities.Translate fraud, identity, and customer requirements into clear and actionable ML work items.Partner closely with ML engineers and data scientists to refine problem statements into feasible deliverables.Define acceptance criteria that reflect real world performance, not just offline model metrics. Embedded ML Team CollaborationServe as the primary product owner for ML and data science teams.Participate actively in model design discussions, prioritization, and tradeoff analysis.Challenge scope, timelines, and modeling approaches when misaligned with business or risk objectives.Propose alternate ideas across data strategy, modeling approaches, workflow design, or deployment patterns. Production Readiness and Lifecycle SupportSupport model lifecycle activities including training, evaluation, deployment, and retraining.Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans.Help define rollout, experimentation, and rollback guardrails. Data and Labeling ExecutionPartner with agent operations and data teams on labeling strategy and data quality.Help define labeling schemas and workflows to support effective model training.Identify risks related to label noise, bias, or insufficient coverage across geographies and document types. Fraud and Adversarial AwarenessIncorporate fraud patterns and adversarial thinking into backlog prioritization.Ensure features and models are resilient to evolving attack vectors such as spoofs, deepfakes, and injection attacks.Support layered and defense in depth approaches rather than single model dependency. Cross Functional CoordinationWork closely with engineering, fraud, compliance, legal, and customer teams.Support internal and external conversations where ML behavior or performance needs explanation.Translate technical constraints into clear delivery expectations for non technical stakeholders.What You Will Need (Required Knowledege, Skills & Abilities): Experience3-5 years of experience in product ownership, product management, or equivalent delivery focused roles.Demonstrated experience supporting ML based products in production.Direct experience working with data science and ML engineering teams. Domain and Technical FluencyStrong working knowledge of computer vision and ML fundamentals.Experience with biometric technologies such as face matching, liveness detection, and spoof prevention.Experience with document verification, document classification, or document fraud detection.Hands-on experience buildin