Applied Data Scientist – Fraud Prevention

We are Creditspring, a new way of borrowing that focuses on its members and provides them with safe and efficient short-term financial products.

We're a fast-growing FCA-regulated consumer credit company. We have members, not customers and we take a lot of pride in that!

As one of the UK’s only subscription finance company in the market, we truly have a unique value proposition. Our mission is very clear; to improve the financial stability and resilience of our members. We do this through the products we provide, the partnerships we have, and our educational content. We want our members, and everyone in the UK to be able to better manage their finances and steer them away from high-cost, unregulated credit options.

About the role

We are currently looking for an experienced and detail-oriented applied data science and business analyst to join our Underwriting data science team with primary focus on fraud detection and mitigation. This is a mid-level applied or ‘full-stack’ data scientist role, ideal for someone with good command of the analytical and machine learning toolkit and desire to drive process and systems change based on the gained insights.

You will be instrumental in shaping company’s fraud prevention initiatives using internal and external data, developing and implementing fraud detection models and providing monitoring and analytics in this area.

This role will collaborate extensively with the colleagues from across the business (Data, Engineering, Underwriting, Operational Risk and Product teams), and is critical to support further platform growth and credit product innovation.

Responsibilities

• Collect, process and analyse large and complex internal and external datasets to identify trends, risks and opportunities

• Design, develop and maintain fraud scoring, identity resolution and credit scoring machine learning models

• Interact with new and existing datasets and solutions providers to run retro analysis, A/B testing and POC exercises

• Review and test applicability of latest developments in fraud modelling to company’s operations (graph and network analytics, behavioural biometrics, real-time detection, adversarial thinking, AI agent networks and other techniques)

• Testing and integration of external API feeds into decisioning flow

• Monitoring, reporting and visualisation of insights and performance metrics

• Cross-team collaboration on incoming queries related to Fraud, AML and KYC verification cases

What you'll need to succeed

• Prior experience in fraud prevention analytics, preferably within an SME or retail lending environment.