Underwriting London Office, Bengaluru Office Hybrid

Applied Data Scientist Fraud Prevention 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 valuable 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 youll need to succeed
- Prior experience in fraud prevention analytics, preferably within an SME or retail lending environment.
- Experience developing and deploying machine learning models in a local and cloud environment
- Strong command of statistical inference and supervised machine learning stack (scikit-learn, pandas, numpy, jupyter). Solid knowledge of Python for data extraction, transformation and analysis
- SQL proficiency for working with data from multiple sources including internal data and external feeds
- Demonstrated success in systems integration and analytics delivery
- Commercial awareness with strong communication skills and the ability to influence stakeholders

Nice to have
- Lending, fintech and regulated sectors work experience
- Working with web applications, cloud data stacks and event driven architecture (we run on ruby on rails, python, aws, github)
- Hands-on working with credit bureau and open banking data. First-hand experience with decisioning SaaS platforms or AI agents

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Applied Data Scientist (Bengaluru)
🏢 creditspring
📍 Bengaluru