Machine Learning Engineer (Payment & Risk) - Global Payment - Singapore

The Global Payment team of Byte Dance provides payment solutions - including payment acquisitions, disbursements, transaction monitoring, payment method management, foreign exchange conversion, accounting, reconciliations, and so on to ensure that our users have a smooth and secure payment experience on Byte Dance platforms.

The Global Payments Compliance team's duty is to establish a comprehensive and strong compliance foundation with sanction screening, transaction monitoring, risk rating system to systematically enable business models, revenue growth, and protect executives from possible legal liabilities for Byte Dance

Responsibilities:

• Refine payment risk feature engineering and fraud evaluation systems;
• Develop universal fraud and account takeover (ATO) detection models for global payment scenarios;
• Explore the application of sequence models, graph networks, and LLMs in risk control scenarios;
• Develop universal machine learning models to identify cross-border and regional payment risks from a global perspective;
• Optimize modeling workflows to improve development/deployment efficiency and reduce maintenance costs;
• Research and apply cutting-edge machine learning algorithms within the risk control domain.

Requirements:

Minimum Qualification(s):

• Bachelor’s degree and above with majors in computer science, computer engineering, statistics, applied mathematics, data science or other related disciplines;
• Solid experience with data structures and algorithms;
• Familiar with at least one framework of Tensor Flow / Py Torch / MXNet and its training and deployment details;
• Strong coding skills in at least one of the following programming languages, e.g. Python, Java, C/C++.

Preferred Qualification(s):

• Minimum 3 years of relevant experience;
• Experience in NLP, graph mining, or fraud detection;
• Experience in credit risk, payment risk, or search/recommendation systems;
• CCF A/B Papers or competition awards are preferred