Data Scientist, Economic Crime Hub

NatWest Group; Manchester; Sur place; GBP 60 000 - 85 000; Plein temps

Résumé du poste

NatWest Group in Manchester is looking for a Data Scientist in the Economic Crime Hub to design and implement data science tools that prevent fraud and scams, reduce customer harm and financial losses, and improve fraud decisioning.

You’ll work with fraud stakeholders and data teams to translate challenges into analytical questions, build scalable pipelines, and apply ML, AI and software engineering in an Agile environment to deliver measurable fraud prevention outcomes.

Qualifications

• A strong STEM degree and experience applying statistical modelling and ML to fraud or risk.
• Ability to translate business problems into analytical questions and measurable outcomes.
• Experience with data pipelines, software engineering practices and Agile development.
• Knowledge of data visualization for stakeholders and familiarity with cloud applications.
• Awareness of model monitoring and governance is desirable.

Responsabilités

• Design and implement data science tools for fraud prevention.
• Collaborate with fraud stakeholders to identify data-led opportunities.
• Develop reusable pipelines and deploy scalable solutions in Agile environments.
• Monitor model performance, data quality, and drift.
• Investigate fraud patterns and drive improvements.

Connaissances

Statistical modelling

Machine learning

Python

Data visualization

Cloud computing

Formation

STEM degree (Math/Physics/Engineering/CS)

Outils

Python

SQL

Cloud platforms

Description du poste

Join us as a Data Scientist, Economic Crime Hub

• You’ll design and implement data science tools and methods which harness our data that use our data to prevent fraud and scams, reduce customer harm and financial losses, and improve the accuracy and efficiency of fraud decisioning
• We’ll look to you to actively participate in the Fraud, Engineering and Data community to identify and deliver opportunities to support the bank’s strategic direction through better use of data
• This is an opportunity to promote data literacy education with business stakeholders supporting them to foster a data driven culture and to make a real impact with your work

What you'll do

As a Data Scientist, you’ll combine statistical analysis, machine learning, generative AI and software engineering to develop practical, responsible solutions to fraud and scam challenges. You’ll work with fraud stakeholders and customer teams to understand their needs, form clear hypotheses and identify data-led solutions that improve fraud detection, reduce false positives, support timely intervention and deliver measurable fraud prevention and operational.

You’ll also be:

• Working with fraud stakeholders to translate fraud and scam challenges into clear analytical questions and measurable outcomes
• Applying a software engineering and product development practices to build reusable pipelines, test changes, and deploy scalable solutions in an Agile environment
• Selecting, building, training and testing machine learning models, fraud strategies and AI applications, balancing fraud detection and business value with customer impact, operational capacity, model risk and ethical considerations

• Monitoring internal and third-party fraud models for performance, data quality, drift and business effectiveness, recommending corrective action where needed
• Investigating emerging fraud patterns, unusual alerts and missed fraud events, turning findings into practical improvements and maintaining clear evidence for governance, audit and regulatory review
  

The skills you'll need

You’ll need a strong academic background in a STEM discipline such as Mathematics, Physics, Engineering or Computer Science. You’ll have experience with statistical modelling and machine learning techniques applied to fraud or other complex risk problems involving rare events.

You’ll also demonstrate:

• The 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 also desirable