Fraud Prevention Data Scientist — Hybrid FinTech

Harnham - Data and Analytics Recruitment; Greater London; Hybrid; GBP 55,000 - 65,000; Full time

Benefits offered by this job

Annual bonus

Contributory pension

Private healthcare

Hybrid working model

Job summary

Harnham - Data and Analytics Recruitment in London is seeking an Applied Data Scientist for Fraud Prevention to build and deploy fraud detection, identity verification, and fraud scoring models within a FinTech context.

You will analyse large datasets, evaluate new data sources and ML techniques, and collaborate with Product, Engineering, Risk, and Data teams to deliver high-impact initiatives that improve customer outcomes and risk strategy.

Qualifications

• Strong commercial experience in fraud analytics, fraud prevention, or financial crime.
• Hands-on experience developing machine learning models using Python.
• Strong SQL and data analysis capabilities.
• Knowledge of statistical modelling and predictive analytics techniques.

Responsibilities

• Develop and deploy fraud detection, identity verification, and fraud scoring models
• Analyse large and complex datasets to identify fraud trends, risks, and opportunities
• Evaluate new data sources, machine learning techniques, and fraud prevention tools
• Build monitoring and reporting solutions to track model and fraud performance
• Collaborate with Product, Engineering, Risk, and Data teams to deliver business-critical initiatives
• Help shape the future direction of fraud analytics across the organisation

Skills

Fraud analytics

Python

SQL

Statistical modelling

Stakeholder communication

Fintech/financial services experience

Job description

Harnham - Data and Analytics Recruitment in London is seeking an Applied Data Scientist for Fraud Prevention to build and deploy fraud detection, identity verification, and fraud scoring models within a FinTech context.

You will analyse large datasets, evaluate new data sources and ML techniques, and collaborate with Product, Engineering, Risk, and Data teams to deliver high-impact initiatives that improve customer outcomes and risk strategy.