Product Data Analytics/Product Data Scientists - Fraud/FinCrime

The client

A global fintech moving and managing money for millions of personal and business customers across international payments, cards and accounts.

You'll join the Financial Crime analytics function, which works out how to stop bad actors without adding friction for genuine customers.

The role

This is a product led analytics role, not a reporting one.

You'll sit with product managers, data scientists, engineers and operations in fraud and AML focused teams.

You'll find opportunities in the data, size them, and make the case for what the team should work on.

The work is technical, but the impact on decisions matters just as much.

You'll be:

• Owning and tracking the key metrics for your product and operational teams, and keeping them accountable through the quarter
• Balancing competing metrics, such as cutting fraud and financial crime while managing operational cost and customer friction
• Scoping new opportunities and suggesting features, rules and models to tackle emerging risks
• Working with Data Scientists/Analytics Engineers to develop detection and mitigation approaches
• Building your own data models and pipelines where needed to answer questions quickly, alongside an established analytics engineering team
• Making data self-serve, so stakeholders can get insight without relying on an analyst
• Influencing senior, domain expert stakeholders and backing your analysis with confidence

Essential

• 5 to 10 years' experience in product analytics, data science or advanced analytics in a professional fraud setting
• Hands-on exposure to financial crime or risk, in at least one of fraud, AML, sanctions and screening, KYC or KYB, transaction monitoring, or anomaly detection
• Advanced SQL that you can write yourself: window functions, CTEs, complex joins, aggregations and query optimisation
• Working fluency in Python
• Practical experience with GenAI tools such as Claude Code or similar in your day to day workflow
• Experience in a product environment where you found and sized problems yourself, rather than only delivering work handed to you
• Strong communication, with the ability to turn complex analysis into clear recommendations
• Data visualisation and storytelling with tools such as Looker, Tableau or Power BI
• Hands-on experience with dbt and Airflow

Preferred

• Other pipeline and orchestration tools such as Dagster or Prefect, or in-house equivalents, will also be considered
• Experience building predictive models
• A background in a fintech, neobank, payments business or product led tech company
• A quantitative degree in maths, statistics, physics, engineering or similar