Technical Product Owner

Your role

What you'll be doing

What We Need

Corpay is currently looking to hire a Technical Product Owner within our Corpay International Vehicle Payments division. This position falls under our Allstar line of business and is located London. We are looking for a delivery-focused Technical Product Owner to turn business problems into valuable, production-ready AI and data products.

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.

How We Work

As a Technical Product Owner, you will work in a hybrid environment, reporting to out London office location 3 days per week.

Corpay will set you up for success by providing:

Assigned workspace in our London office location

Company-issued equipment

Formal, hands-on training

Role Responsibilities

The responsibilities of the role will include:

• •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.

Product ownership and 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.

AI, data and analytical fluency

• •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.
• •Ability 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.

Business and stakeholder leadership

• •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.

Governance and quality

• •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. Corpay maintains structured compensation practices and regularly reviews pay to support complianc