Applied Data Scientist – Fraud Prevention

About the role

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.

What you'll do

• 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
• Testing and integration of external API feeds into decisioning flow

What you need

• Machine learning
• Python
• SQL
• Scikit-learn
• Pandas
• NumPy
• Any graduate

Good to have

• AWS
• Ruby
• Git
• AML
• KYC
• Operational risk