Fraud Analytics Scientist – Economic Crime Hub

NatWest Group; United Kingdom; On-site; GBP 60,000 - 90,000; Full time

Job summary

NatWest Group is seeking a Data Scientist within the Economic Crime Hub in the United Kingdom. You will design and implement data science tools that harness data to prevent fraud, reduce customer harm and financial losses, and improve fraud decisioning.

You'll work with fraud stakeholders, engineering and data teams to translate challenges into analytical questions, build reusable pipelines and deploy scalable models in an Agile environment, balancing risk with business value and customer impact.

Qualifications

• Strong academic background in STEM (Math, Physics, Engineering or CS).
• Experience with statistical modelling and ML for fraud or risk problems.
• Ability to translate fraud challenges into analytical questions with measurable outcomes.
• Experience in software engineering fundamentals and programming.
• Experience with Cloud applications.
• Experience in data storytelling for stakeholders.
• Knowledge of Large Language Models and agentic AI and governance controls is desirable.

Responsibilities

• Design and implement data science tools to prevent fraud and scams.
• Work with fraud stakeholders and customer teams to identify data-led solutions.
• Develop reusable pipelines and deploy scalable models in an Agile environment.
• Monitor models for performance and data quality; adjust as needed.
• Investigate emerging fraud patterns and document findings for governance.

Skills

Statistical modelling

Machine learning

Generative AI

Software engineering

Cloud applications

Data visualisation

Programming

Education

STEM degree in Mathematics / Physics / Engineering / CS

Job description

NatWest Group is seeking a Data Scientist within the Economic Crime Hub in the United Kingdom. You will design and implement data science tools that harness data to prevent fraud, reduce customer harm and financial losses, and improve fraud decisioning.

You'll work with fraud stakeholders, engineering and data teams to translate challenges into analytical questions, build reusable pipelines and deploy scalable models in an Agile environment, balancing risk with business value and customer impact.