Blockchain Risk Algorithm Intern

Bybit·APAC - Remote·remote global
crypto:applicationengineeringIC2Group Risk Control
Compensation
Not disclosed
About Us Established in March 2018, Bybit is one of the fastest growing cryptocurrency derivatives exchanges, with more than 70 million registered users. We offer a professional platform where crypto traders can find an ultra-fast matching engine, excellent customer service and multilingual community support. We provide innovative online spot and derivatives trading services, mining and staking products, as well as API support, to retail and institutional clients around the world, and strive to be the most reliable exchange for the emerging digital asset class. Our core values define us. We listen, care, and improve to create a faster, fairer, and more humane trading environment for our users. Our innovative, highly advanced, user-friendly platform has been designed from the ground-up using best-in-class infrastructure to provide our users with the industry's safest, fastest, fairest, and most transparent trading experience. Built on customer-centric values, we endeavour to provide a professional, 24/7 multi-language customer support to help in a timely manner. As of today, Bybit is one of the most trusted, reliable, and transparent cryptocurrency derivatives platforms in the space. We are seeking a skilled machine learning engineer with a security or risk control background to join our team. As a Risk Control Algorithm Engineer Intern, you will need to apply statistical and machine learning techniques for anomaly detection. Job Description Conduct data exploration and statistical analysis based on a deep understanding of business processes, and develop machine learning models to detect various business threats. Develop anti-cheating, anti-fraud, and adversarial technologies using data mining, machine learning, deep learning, and unsupervised learning techniques to address business security issues. Collaborate with product and technical teams to deploy models, ensuring continuous maintenance, iterative optimization, and product upgrades. Drive algorithmic i