Data Product Manager
Experian is hiring a Data Product Manager in London, United Kingdom. Level rates it ; you can apply on Level.
Level's read on this job
- Level rates this AI product manager role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions data product management, roadmap planning and analytics among its tools and skills.
- Level classes it as a senior role.
- Experian has 398 other open roles on Level.
- This posting went up in the last few days.
AI in this role
Drive the data product strategy and roadmap connecting customer priorities with data platform, analytics, and AI capabilities.
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.
At Experian Consumer Services, data is a core product capability. Our products depend on reliable event capture, trusted customer data, reusable data products, analytics and AI to improve customer experiences and create value through our Marketplace.
We are looking for a Data Product Manager to turn our data strategy into a clear, prioritised and measurable portfolio of data products.
This role will connect customer and commercial priorities with the domain teams, Data Platform, Engineering, Analytics, AI, Operations, Finance, Security and Governance capabilities required to deliver them. You will create clarity across priorities, ownership, dependencies, risks, adoption and value.
Working across the UK and Hyderabad, you will help us move from a central, request-driven data model towards domain-owned data products, self-service analytics and stronger data accountability.
What you will do:
Shape the data product strategy
- Translate ECS strategy, the FY27 product roadmap, Data 4.0 and AI priorities into a coherent data product strategy and roadmap.
- Prioritise data initiatives based on customer value, commercial impact, feasibility, dependencies and risk.
- Ensure that data investments support acquisition, activation, engagement, Marketplace conversion, lender matching, monetisation and platform reliability.
Establish product ownership and lifecycle discipline
- Define the minimum standards for a data product, including purpose, consumers, inputs, outputs, KPI definitions, quality expectations, freshness, privacy classification, owner, support model and retirement plan.
- Create clear ownership across domains, central teams and shared capabilities.
- Identify opportunities for reuse across domains and prevent duplicated metrics, pipelines and reporting logic.
- Support the transition to a domain-owned and self-service data operating model.
Connect strategy to execution
- Maintain one integrated view of use cases, domain adoption, source onboarding, data products, migration, dual running, consumer cutover and legacy decommissioning.
- Work with Product, Engineering and Analytics leads to sequence delivery around the highest-value use cases and dependencies.
Manage risks proactively
- Identify delivery, data quality, privacy, security, capacity, supplier and operational risks before they become incidents or delivery failures.
- Maintain clear risk and dependency views with leading indicators, mitigation actions, accountable owners and decision dates.
Measure adoption and value
- Define how data product success will be measured, including adoption, reuse, quality, freshness, time to insight, delivery cycle time and operational reliability.
- Work with Product, Analytics and Finance to connect data delivery to business outcomes such as leads, conversion, upsell, engagement, retention, revenue per user, productivity and platform cost.
- Track value delivery against the business case and recommend reprioritisation when assumptions or market conditions change. Ensure that priority use cases have evidence of consumer adoption, business impact and readiness for continued operation.
- Significant experience in product management, especially data products, data platforms.
- Practical experience with domain ownership, self-service data, data governance, data migrations or data operating model change, and experience working with data engineering teams.
- Experience managing a portfolio of interconnected initiatives rather than a single product backlog.
- Strong understanding of the data lifecycle, including event design, ingestion, modelling, data contracts, quality, metadata, lineage, analytics and activation.
- Enough technical fluency to challenge assumptions, understand trade-offs and connect product decisions to architecture, quality, privacy, reliability and cost.
- Strong commercial judgement and experience linking product or data investments to measurable business value.
- Proven ability to influence senior stakeholders and delivery teams without relying on formal authority.
- Exceptional organisation skills, with a clear and repeatable approach to priorities, decisions, dependencies, risks and follow-through.
- Excellent written and verbal communication, including the confidence to communicate difficult news early and constructively.
- Comfort working in a complex, changing environment where information is sometimes incomplete and decisions need to move forward.
Benefits package includes:
- Hybrid working - 40% office based
- Great compensation package and discretionary bonus
- Core benefits include Pension, Bupa healthcare, Sharesave Scheme and more
- 25 days annual leave with 8 bank holidays and 3 volunteering days. You can purchase additional annual leave.
Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Recruitment Fraud Awareness - Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @experian.com email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. To apply for roles or verify opportunities, please visit experian.com/careers
Grade: D/EB8
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How we rate this
Data Product Manager at Experian rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI product manager jobs at this level
Pay
- Typical AI product manager job
- $178k
This posting doesn't list pay. Use the typical AI product manager figure when pay comes up. See AI product manager jobs
Questions you could be asked
- Tell me about a project where data product management was part of your work. What did you do?
- Tell me about a project where roadmap planning was part of your work. What did you do?
- Tell me about a project where analytics was part of your work. What did you do?
- Tell me about a project where strategy was part of your work. What did you do?
- Describe a typical day in a role like this one: which parts run through AI directly?
Adapt your resume
- List these exact terms on your resume: Data Product Management, Roadmap Planning, Analytics, and Strategy. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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