Data Scientist, Economic Crime Hub

NatWest Group

. Design and implement data science tools and methods using data to prevent fraud and scams, reduce customer harm and financial losses, and improve fraud decisioning accuracy and efficiency .

Posted 10/7/2026full-timeRemote • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in statistical modeling, machine learning, and data-driven decision-making to address fraud and scam challenges. Proficient in building scalable solutions and promoting data literacy within organizations.

Highest-signal resume keywords

Statistical ModelingMachine Learning TechniquesData VisualizationCloud ApplicationsAgile Environment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Science ToolsFraud DecisioningProgramming LanguageSoftware Engineering FundamentalsModel MonitoringGenerative AIData AnalysisHypothesis TestingData Pipeline DevelopmentRisk Governance

Soft Skills

CollaborationCommunicationProblem SolvingStakeholder EngagementData Literacy Promotion

Tools & Technologies

Cloud ApplicationsMachine Learning ModelsAI ApplicationsData PipelinesStatistical Software

Industry Keywords

Fraud PreventionScam AnalysisData-Driven CultureGovernanceRegulatory Review

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Design and implement data science tools and methods using data to prevent fraud and scams, reduce customer harm and financial losses, and improve fraud decisioning accuracy and efficiency
Participate in the Fraud, Engineering and Data community to identify and deliver data-driven opportunities aligned with the bank’s strategic direction
Promote data literacy education with business stakeholders and support a data-driven culture
Combine statistical analysis, machine learning, generative AI and software engineering to develop practical, responsible solutions to fraud and scam challenges
Work with fraud stakeholders and customer teams to understand needs, form hypotheses and identify data-led solutions
Translate fraud and scam challenges into analytical questions and measurable outcomes
Build reusable pipelines, test changes and deploy scalable solutions in an Agile environment
Select, build, train and test machine learning models, fraud strategies and AI applications
Monitor internal and third-party fraud models for performance, data quality, drift and business effectiveness
Investigate emerging fraud patterns, unusual alerts and missed fraud events, and turn findings into practical improvements
Maintain evidence for governance, audit and regulatory review

Requirements

What you’ll need

Strong academic background in a STEM discipline such as Mathematics, Physics, Engineering or Computer Science
Experience with statistical modelling and machine learning techniques applied to fraud or other complex risk problems involving rare events
Ability to use data to solve business problems from hypotheses through to resolution
Experience using programming language and software engineering fundamentals
Experience of Cloud applications and options
Experience in synthesising, translating and visualising data and insights for key stakeholders
Experience in model monitoring, model-risk governance and documenting analytical decisions for review and challenge is desirable
Knowledge of how Large Language Models and agentic AI can support fraud and scam analysis, and the controls required to manage the risks of using those applications, is desirable

Benefits

Comp & perks

Remote First working arrangement