Own 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.
Participate actively in model design discussions, prioritization, and tradeoff analysis.
Support model lifecycle activities including training, evaluation, deployment, and retraining.
Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans.
Partner with agent operations and data teams on labeling strategy and data quality.
Incorporate fraud patterns and adversarial thinking into backlog prioritization.
Work closely with engineering, fraud, compliance, legal, and customer teams.

3-5 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.
Strong 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 building ML based products in the biometric and document identity space is highly valuable.
Ability to evaluate tradeoffs between different modeling and data approaches without being a data scientist.
Comfortable pushing back on ML teams when solutions are over engineered, misaligned, or not production ready.
Able to propose alternate approaches grounded in data availability, fraud realities, and delivery constraints.
Strong attention to detail and a bias toward shipping reliable and measurable capabilities.
Able to clearly articulate ML concepts, risks, and tradeoffs to non technical stakeholders.
Comfortable supporting customer facing or internal discussions around model behavior and limitations.
Able to document requirements and acceptance criteria with precision.

Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics
Financial future: retirement/pension plan contributions, MTK stock plan participation
Income protection: life event & disability coverage
Paid time off: generous annual leave, company holidays, volunteer time off
Learning: e-learning license, tuition reimbursement, hackathons
Home office setup allowance
Additional/optional benefits: pet insurance, identity theft protection, legal assistance

Product Owner – Machine Learning

Key skills