ByteDance's E-Commerce Risk Control (ECRC) team aims to protect users, ensure the integrity of the e-commerce ecosystem, and provide a safe shopping experience by preventing and detecting risks and mitigating their negative impact on customers and selling partners.
Requirements
- Proficiency in modern machine learning theories and applications, including ensemble trees, deep neural networks, transfer/multi-task learning, reinforcement learning, graph theory, and unsupervised learning.
- Experience with Python, SQL/Hive, and Hadoop
- Strong understanding of data structures and algorithms, with excellent problem-solving ability
- Demonstrated software engineering experience from previous internship, work experience, coding competitions, or publications
- Internship experience or research experience, especially in e-commerce, risk control domian
- Curiosity towards new technologies and entrepreneurship
- High levels of creativity and quick problem-solving capabilities
Responsibilities
- Develop and implement innovative machine learning algorithms to manage business risks in Bytedance 's products and platforms.
- Prototype and explore novel solutions, conduct experiments to validate hypotheses, and provide insights to Product and Tech teams.
- Collaborate with multidisciplinary teams to enhance current automation processes.
- Build efficient data querying infrastructure for real-time and offline analysis.
- Identify opportunities to strengthen risk defense solutions.
- Define risk control metrics and promote data-driven practices.
- Document technical processes and effectively communicate results through writing, visualizations, and presentations.
Other
- Currently pursuing a PhD in Computer Science Engineering, Operations Research, or related fields.
- Able to commit to working for 12 weeks during Summer 2026
- Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment.
- Detail oriented, effective time management, and strong analytical skills
- Graduating December 2026 onwards with the intent to return to degree program after the completion of the internship.
