Role: Data scientist ML specialist
Unit: Trading ECN Liquidity

Prevent fraud and prohibited users in KYC through machine learning and data-driven decision making.

Main performance number: Fraud detection mistake %
Second performance number: Uptime of real time KYC service %
Third performance number: Cost of KYC service infrastructure

• Fraud detection : Build and maintain ML models to prevent fraud and prohibited users from passing KYC
• Development of fraud detection algorithms : Continuously improve the quality of fraud detection algorithms to outpace fraudsters.
• AML policy implementation : Ensure up-to-date connection to all AML databases and use them to prevent prohibited users from using LATOKEN services.
• Creation of highly scalable and reliable pipelines : Design production infrastructure and roll out models to ensure scalability up to 1000+ online KYC users and 99.99% uptime.
• Cost management : Minimize the cost of KYC service infrastructure.
• Collection of inputs from relevant LATOKEN units : Interact with and collect inputs from all LATOKEN units impacted by KYC fraud.

Requirement skills and experience:

  • Python, ML, DL, REST API
  • GPGPU programming using CUDA
  • Cloud computing (AWS, Azure, or GCP);
  • Computer vision/image processing
  • Rolling out models into production/devops

Would be a plus:

  • Software development and optimization
  • Antifraud, KYC, anomaly detection systems, predictive modeling
  • Implementing algorithms from research papers.

Desired background:

  • Bachelor’s or Master's degree in computer science, applied mathematics, statistics, machine learning etc.
  • 2+ years work experience in fintech
  • Creative problem solver
  • Integrity and high ethical standards