Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead AI initiatives, turning complex problems into impactful AI products.
- Company: A leading financial services organisation focused on AI and data capabilities.
- Benefits: Contract role outside IR35 with competitive pay and growth opportunities.
- Why this job: Shape the future of banking with innovative AI solutions in a dynamic environment.
- Qualifications: Experience as a Product Owner in AI or data-driven products, preferably in finance.
- Other info: Collaborate with diverse teams and ensure responsible AI practices.
The predicted salary is between 36000 - 60000 £ per year.
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
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
Product Owner in City of London employer: Qurated
Contact Detail:
Qurated Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Product Owner in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the banking and AI sectors on LinkedIn. Join relevant groups, attend webinars, and don’t be shy about sliding into DMs. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio or case studies showcasing your experience with AI products. Highlight how you’ve turned complex problems into solutions. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your knowledge of machine learning concepts and their applications in banking. Be ready to discuss how you’ve navigated compliance and risk in previous roles. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure your application stands out by tailoring it to the specific role.
We think you need these skills to ace Product Owner in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Product Owner. Highlight your experience with AI-driven products and any relevant banking or financial services background. We want to see how your skills align with the job description!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how you can bridge the gap between business and technology. We love seeing genuine enthusiasm for the role, so let your personality come through!
Showcase Your Achievements: When detailing your past experiences, focus on specific achievements that demonstrate your ability to deliver high-impact AI initiatives. Use metrics where possible to show how you've made a difference in previous roles. We appreciate results-driven candidates!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re proactive and keen to join our team at StudySmarter!
How to prepare for a job interview at Qurated
✨Know Your AI Stuff
Make sure you brush up on your understanding of machine learning concepts and how they apply to banking. Be ready to discuss how you've previously translated complex business problems into AI use cases, as this will show your ability to bridge the gap between technical and non-technical stakeholders.
✨Showcase Your Product Ownership Skills
Prepare examples that highlight your experience in owning product vision and roadmaps for AI-driven products. Think about specific instances where you prioritised delivery across data readiness and model development, and be ready to explain your decision-making process.
✨Understand the Regulatory Landscape
Since this role is in a heavily regulated environment, it’s crucial to demonstrate your knowledge of compliance and risk management. Familiarise yourself with Responsible AI principles and be prepared to discuss how you've ensured fairness, transparency, and accountability in your previous projects.
✨Metrics Matter
Be ready to talk about how you define and track success metrics for AI products. Think of concrete examples where you measured business value, user adoption, and model performance, and how those metrics influenced your product decisions.
