At a Glance
- Tasks: Transform business challenges into impactful AI and data products.
- Company: Join Corpay, a leader in innovative commercial payments.
- Benefits: Enjoy competitive salary, hybrid work, and professional growth opportunities.
- Other info: Dynamic team environment with a commitment to diversity and inclusion.
- Why this job: Be at the forefront of AI solutions and drive real change.
- Qualifications: 2+ years in product ownership or similar roles with tech focus.
The predicted salary is between 50000 - 60000 £ per year.
Your role involves being a delivery-focused Technical Product Owner within our Corpay International Vehicle Payments division. You will sit between business stakeholders, Data Science, Engineering and Technology teams: clarifying opportunities, gathering and structuring requirements, prioritising delivery, and ensuring that AI solutions are practical, measurable, governed and adopted.
A core part of the role is helping the Data Science team understand the business context behind the data—what decisions need improving, what questions must be answered, what data is required, and how success will be measured. This is not a pure strategy role; you will own the delivery discipline that takes AI initiatives from discovery and analysis through experimentation, implementation, launch and continuous improvement.
As a Technical Product Owner, you will work in a hybrid environment, reporting to our London office location 3 days per week. Corpay will set you up for success by providing an assigned workspace in our London office location, company-issued equipment, and formal, hands-on training.
Role Responsibilities
- Owning and maintaining the backlog and roadmap for AI- and data-enabled products, prioritising work based on business value, user need, feasibility, data readiness, risk, and delivery dependencies.
- Partnering with business teams to identify high-value problems, mapping current processes, gathering requirements, and translating ambiguous needs into clear product outcomes.
- Working closely with Data Scientists to convert business questions into data-analysis requirements, including hypotheses, measures, data definitions, segmentation needs, assumptions, and decision criteria.
- Supporting data discovery and analysis by ensuring teams have the right business context, access to subject-matter experts, clear definitions, and timely feedback on findings.
- Defining epics, features, user stories, acceptance criteria, and non-functional requirements for AI and data products.
- Coordinating delivery across Data Science, Data Engineering, Software Engineering, Architecture, Security, Risk, Compliance, and third-party suppliers where relevant.
- Leading discovery, proof-of-concept, and pilot activity; establishing success measures; and making evidence-based recommendations for scaling, iterating, pausing, or retiring solutions.
- Facilitating prioritisation, backlog refinement, sprint planning, demos, and stakeholder reviews; removing blockers; and surfacing risks, dependencies, and trade-offs early.
- Defining how AI products and models will be evaluated in production, including user feedback, operational performance, quality, accuracy, adoption, and realised benefits.
- Ensuring responsible AI, data protection, security, model-risk, and governance requirements are being built into delivery rather than being added at the end.
- Supporting go-live, UAT, change management, training, and operational handover, ensuring solutions are being adopted and continuously improved.
Qualifications
- 2+ years of relevant professional experience as a Technical Product Owner, Product Manager, Business Analyst, Delivery Analyst, or in a similar role supporting technology, data, or digital product delivery.
- Proven experience as a Technical Product Owner, Product Manager, Business Analyst or similar role in a complex technology, data or digital delivery environment.
- Strong backlog ownership: able to break strategic goals into prioritised, well-defined epics, features and user stories.
- Experience delivering products end-to-end, from problem definition and discovery to launch, adoption and benefits realisation.
- Excellent delivery management skills, including dependency management, risk and issue management, roadmap planning and stakeholder reporting.
- Comfortable operating in agile, iterative product-development environments and working directly with engineering teams.
- Strong understanding of AI, machine learning and generative AI concepts, including their practical limitations and risks.
- Experience working alongside Data Science, Data Engineering or Analytics teams on data-driven products or decision-making.
- Able to translate business objectives into analytical requirements: metrics, hypotheses, datasets, business rules, success thresholds and evaluation criteria.
- Able to interpret data-analysis outputs, ask useful questions, challenge assumptions and communicate findings back to non-technical stakeholders.
- Familiarity with AI development lifecycles: problem framing, data preparation, experimentation, model evaluation, deployment, monitoring and iteration.
- Understanding of common enterprise AI patterns, such as LLMs, retrieval-augmented generation, prompt design, AI evaluation and human-in-the-loop workflows, is desirable.
- Technical literacy sufficient to work credibly with Data Science and Engineering teams; hands-on coding or data-querying experience is advantageous but not essential.
- Strong workshop facilitation and requirements-gathering skills, with the ability to uncover the underlying business problem rather than simply document requested solutions.
- Able to communicate clearly across technical and non-technical audiences, translating business needs into delivery requirements and technical findings into actionable decisions.
- Proven ability to influence stakeholders, manage competing priorities and create alignment across multiple teams.
- Commercially minded and outcome-focused, with a bias toward measurable value over technology for its own sake.
- Comfortable with ambiguity, able to bring structure, clarity and momentum to complex work.
- Understanding of data privacy, security, responsible AI, model risk, bias, hallucination, auditability and human oversight.
- Experience balancing innovation and speed with quality, resilience, compliance and operational readiness.
- Strong focus on measurable outcomes, including adoption, productivity, customer or colleague experience, model quality and financial or operational value.
What success looks like
- Business problems are clearly framed, prioritised and connected to measurable outcomes.
- Data Scientists receive clear, usable analytical requirements and sufficient business context to produce relevant insight.
- AI and data initiatives move efficiently from idea to validated pilot to operational product.
- Delivery teams have a well-managed backlog, clear priorities and early visibility of risks and dependencies.
- Stakeholders understand the value, limitations and performance of AI products.
- Products are adopted, monitored and improved based on evidence—not assumed value.
- AI solutions are safe, governed and fit for use in the organisation’s operational context.
Ideal profile
You are a pragmatic, analytical product owner who enjoys working close to delivery teams. You can move between a business workshop, a data-analysis review and an engineering planning session without losing sight of the user problem or desired outcome. You do not need to be the person building the model, but you understand enough about data and AI to ask the right questions, shape high-quality requirements and help teams make sound decisions.
The salary range for this position is 50,000 – 60,000 gross per year. The final offer will be determined based on objective, gender-neutral criteria including relevant skills, professional experience, and qualifications.
Technical Product Owner in London employer: Corpay
Corpay is an exceptional employer that fosters a dynamic work culture centred on innovation and collaboration, making it an ideal place for a Global Travel Operations Specialist. With a strong commitment to employee growth, we offer comprehensive training and development opportunities, ensuring that our team members thrive in their careers. Located in the heart of a vibrant city, our office provides a stimulating environment where you can engage with diverse colleagues and contribute to shaping the future of commercial payments.
StudySmarter Expert Advice🤫
We think this is how you could land Technical Product Owner in London
✨Join Product Management Meetups
Get involved in local product management meetups or workshops. These events are perfect for meeting industry folks, sharing ideas, and staying updated on trends. Plus, you never know who might be hiring—it's a fantastic way to make connections that could lead to a job at places like Corpay!
✨Show Off Your Product Sense
Create case studies or mini-projects showcasing your product management skills, and share them on platforms like Medium or LinkedIn. This not only puts your skills on display but also boosts your visibility in the product community. Imagine how impressed the hiring team at Corpay would be by your initiative!
✨Utilise Online Communities
Dive into online product management communities like Product Coalition or Mind the Product. Engage in discussions, ask questions, and share your insights. These platforms are goldmines for networking and finding hidden job opportunities—many companies often scout talent from within these circles.
✨Leverage Your University Network
If you’ve recently graduated or are still in uni, tap into your alumni network for connections in product management. Many universities have their own job boards and affinity resources to help graduates land roles. Don't forget to keep an eye out for job openings at Corpay through your school's career services!
We think you need these skills to ace Technical Product Owner in London
Some tips for your application 🫡
Show Off Your Product Passion:When applying for a product management role like Technical Product Owner, let your passion for developing products shine through in your cover letter. Share specific examples of products you've managed, how you solved user needs, and any successful outcomes you've achieved. This is your chance to showcase your understanding of the product lifecycle!
Highlight Your Cross-Functional Skills:Product management isn't just about understanding the product; it’s about collaborating with different teams! Make sure to emphasise your experience working with developers, designers, and marketers. Use your CV to showcase your ability to bridge gaps between these areas, and include relevant experiences that demonstrate your communication and leadership skills!
Include Your Metrics and Achievements:In a full-time product management application, data speaks volumes! Quantify your achievements wherever possible. Did you increase user retention by a certain percentage? Launch a product ahead of schedule? Include these metrics in your CV to paint a picture of your impact and effectiveness in previous roles.
Tailor Your CV to the Role:Make sure your CV is tailored for the Technical Product Owner position at Corpay. Use keywords from the job description and ensure your relevant experiences are front and centre. Highlight any certifications or relevant training you’ve completed that will make you stand out as a strong candidate for the role. And remember, we’re excited to see your application on our website!
How to prepare for a job interview at Corpay
✨Understand the Product Life Cycle
As a product management candidate, we need to get our head around the complete product life cycle. Be prepared to discuss real-world examples of how you’ve managed product development from ideation to launch. Bring specific insights on tools like JIRA or Trello that can help streamline these processes.
✨Showcase Your Cross-Functional Skills
Product management is all about collaboration. We should be ready to highlight how we’ve worked across teams—think marketing, engineering, and design. Prepare to discuss scenarios where you had to mediate differing opinions and how you got everyone on board with a shared vision.
✨Prepare for Case Studies
In a full-time role, we can expect to encounter case study questions during our interviews. Practise solving hypothetical product problems on the spot, such as prioritising features for a new app or improving user engagement metrics. This will show our analytical thinking and decision-making skills.
✨Know Your Metrics
Let’s face it, numbers are our best friends in product management. We should prepare to discuss key performance indicators (KPIs) and how we've used analytics to inform product decisions. Dive into examples where data has driven our strategy for improvements or justified product changes.
