Product Manager - Graph Risk Intelligence

Wise is a global technology company, building the best way to move and manage the world’s money.

Min fees. Max ease. Full speed.

Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.

As part of our team, you will be helping us create an entirely new network for the world's money.

For everyone, everywhere.

More about our mission and what we offer.

We are looking for a highly technical, hands-on Principal Product Manager to lead the Graph Risk Intelligence team. You will define how Wise ingests, stores, computes and learns from connected data, then turn that strategy into capabilities that teams can use in production.

You will build close relationships with PMs, data scientists, engineers and operational teams to understand their goals and constraints. You will balance immediate use cases with the long-term platform, focusing on speed of experimentation, cost efficiency, reliability and measurable improvements in decision quality.

This is a senior individual contributor role with influence across Risk Intelligence and the wider organisation. The initial team will include an engineering lead, machine learning engineers and backend engineers. You will give the team clear problem statements, stay close to the technical work and create the product principles for engineers and data scientists to own important problems.

Set the product direction. Create a clear vision and strategy for graph capabilities at Wise. Translate it into a focused roadmap across data ingestion, storage, computation, signal generation, modelling, analytics and serving.

Understand the users and problems that matter. Work directly with data science, engineering and operations teams to understand how they investigate relationships, build models and make decisions. Find common needs that should be solved once as a platform.

Make graph experimentation easier. Help teams ingest and augment graph data, test graph signals and move successful ideas from experimentation into reliable production use.

Advance graph intelligence. Guide and enable the use of graph algorithms and graph-based machine learning, including when simpler heuristics or classical methods are the better choice. Make the technical and product trade-offs understandable to others.

Make difficult choices. Balance efficacy, speed, platform reuse, engineering complexity, model risk, operating cost and maintenance. Explain priorities clearly and decide what to build, improve or stop.

Stay close to delivery. Work with engineering and machine learning from discovery through experiment, launch and iteration. Challenge assumptions, contribute to technical design, remove blockers and help the team deliver at pace.

Lead adoption across Wise. Build alignment with teams in anti-financial crime, underwriting, customer support, finance and customer relationship management. Identify dependencies early and help teams adopt shared graph capabilities successfully.

Own measurable outcomes. Define the metrics for platform adoption, experimentation speed, model performance, cost, reliability and user outcomes. Use evidence to assess progress and determine what to improve next.

What this role offers

Build a new product area. Establish the team, its operating model and its product strategy from the ground up.

Work on high-impact problems. Shape how Wise uses connected data and advanced techniques to improve risk decisions and customer outcomes.

Influence several parts of Wise. Create reusable capabilities that can support anti-financial crime, underwriting, customer support, finance and customer relationship teams.

Learn with strong technical teams. Work closely with machine learning engineers, backend engineers, data scientists and product teams that are exploring advanced methods.

Shape the next stage of Risk Intelligence. Help the area learn where deeper research investment can create practical value for Wise.

You have built AI or machine learning products. You have led a product or technical area and can show how your decisions and delivery improved user, risk or business outcomes.

You are a builder. You go beyond coordination. You can challenge technical choices, propose experiments, work directly with data and contribute meaningfully to solution design.

You bring strong product judgement. You can turn an ambiguous set of needs into a clear problem, strategy and roadmap, then make sound choices when priorities compete.

You have technical depth. You understand machine learning systems, data pipelines and backend platforms well enough to discuss algorithms, architecture, model behaviour and implementation trade-offs with engineers and data scientists.

You use data yourself. You can frame problems with metrics, analyse complex data, validate hypotheses and build or interrogate dashboards without depending on others for every answer.

You start with users. You understand the needs of internal and external users, test assumptions early and use their behaviour and constraints to guide product decisions.

You lead through influence. You can give a technical team an inspiring direction, challenge constructively and align teams whose goals or timelines differ.

You communicate with precision. You can explain a complex method in simple terms, give a concise view of a problem and go deep when the audience needs detail.

You learn quickly and deeply. You follow advances in AI and machine learning because you want to understand how they work, where they are useful and where they are not.

Graph expertise. Experience researching or shipping graph algorithms, graph neural networks, embeddings, knowledge graphs or other products built on connected data.

Machine learning depth. Experience researching, training or deploying deep learning models, with a clear view of why a chosen method performed better than the alternatives.

Platform experience. Experience building data or machine learning platforms used by several product or research teams.

Relevant domain experience. Experience applying machine learning to financial crime, fraud, credit, trust and safety, customer support or another high-stakes decision environment.

Strong STEM grounding. A degree in a relevant technical field or equivalent depth gained through hands-on work.

Additional Information

Wise serves customers around the world, and we want our teams to reflect a wide range of backgrounds and perspectives. We aim to give every candidate a fair opportunity to show their skills and every employee the support to contribute and grow.

If this role excites you but your experience does not match every point above, we still encourage you to apply. We are interested in the problems you have solved, the choices you made and the impact you created.

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.

We're proud to have a truly international team, and we celebrate our differences.

Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

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