Product Owner

CompanyQurated

LocationUnited Kingdom

Posted At1/22/2026

UK Visa Sponsorship Analytics

Analytics are greyed out due to low classification confidence (36.0%).

Occupation Type

Garage managers and proprietors

Occupation Code Skill LevelMedium Skilled

Sponsorship EligibilityIneligible for Sponsorship

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Description

AI Product Owner – Banking

The opportunity

We’re working with a leading financial services organisation that is scaling its AI and data capabilities across the bank. They’re looking for an experienced AI Product Owner to take ownership of high‑impact AI initiatives, helping turn complex business problems into trusted, compliant, production‑ready AI products. This would be a contract role, outside IR35.

This role sits at the heart of business, technology, data science, and risk, and is ideal for someone who is comfortable delivering in a regulated, high‑scrutiny environment.

What you’ll be doing

Own the product vision, roadmap, and backlog for one or more AI‑driven products or platforms
Translate business goals into AI use cases, hypotheses, and measurable outcomes
Work hands‑on with data scientists, ML engineers, and data engineers to shape data and model requirements
Prioritise delivery across data readiness, model development, experimentation, and release

Act as the bridge between business stakeholders, technology, risk, legal, and compliance

Ensure AI solutions are explainable, usable, and appropriate for customers and colleagues

Embed Responsible AI principles, including fairness, transparency, and accountability

Define and track success metrics across business value, user adoption, and model performance

Support AI products through deployment, monitoring, and continuous improvement

What we’re looking for

Proven experience as a Product Owner / Product Manager delivering AI‑ or data‑driven products
Experience working in banking or financial services (or other heavily regulated environments)
Strong understanding of:
Machine learning concepts and limitations (no coding required)
Data quality, model evaluation, and performance trade‑offs
Comfortable operating in Agile / Scrum environments
Ability to explain complex AI topics clearly to non‑technical stakeholders

Strong judgement around risk, ethics, and governance

Nice to have

Experience in areas such as fraud, credit risk, financial crime, customer operations, or GenAI
Exposure to model risk management or regulatory engagement
Experience working with AI platforms, MLOps, or enterprise data ecosystems