Machine Learning Engineer (Compliance) - Global Payment - Singapore

The Global Payment team of ByteDance 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 ByteDance platforms including TikTok.

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 ByteDance (e.g. TikTok Live, TikTok Shop).

Responsibilities

- Understand each business line and payment scenarios well, design Red Flags to monitor potential compliance risk scenarios, use AI to identify and control financial crimes;

- Optimize and iterate compliance strategy standardly, systematically, timely and proactively;

- Design and develop machine learning algorithms to detect suspicious transactions and accounts effectively and assist investigation procedures efficiently;

- Discover potential syndicates and concerted behavior communities from large-scale graph data based on graph neural networks;

- Follow up the latest research progress in the fields of anti-money laundering and fund security in the finance / technology industry, and apply it in the work.

Minimum Qualifications:

- Bachelor 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 TensorFlow / PyTorch / MXNet and its training and deployment details

- Strong coding skills in at least one of the following programming frameworks, e.g. Python, Java, C/C++, etc.;

Preferred qualifications:

- Minimum 3 years of relevant experience.

- Familiar with big data related frameworks and applications, those who are familiar with MR or Spark are preferred.

- Experience with LLM/LVM projects, such as risk identification and efficiency improvement, is preferred.

- CCF A/B Papers or competition awards are preferred.