Research and Develop Algorithms: Identify various fraud orders in e-commerce search and recommendation, design and develop effective risk control strategies and models to guarantee user experience and seller benefit.
Model Optimisation: Explore, innovate, and optimise risk control models to improve precision, recall, and scalability.
Data Analysis: Conduct in-depth analysis of business data, user behaviour data, and seller/item information, leveraging various anomaly detection methods for modelling.
Business and Algorithm Integration: Align risk control strategies with advertising business objectives, establish a robust anomaly monitoring system, and quickly detect and address risks.
Industry Trends Monitoring: Stay updated on the latest trends in risk control and anomaly detection technologies, exploring innovative algorithms.
Requirements:
Bachelor's degree or higher in Computer Science or a related field
Minimum 2 years of work experience in AI engineering or anti-fraud systems.
Hands on experience in applying or fine-tuning LLM, familiar with reinforcement learning, etc
Excellent coding abilities
Solid foundation in machine learning theory and familiar with common anti-fraud models, proficient in using deep learning frameworks, such as TensorFlow/Caffe/MXNet/PyTorch
Detail-oriented, highly organised, and quick to learn.
Strong communication and teamwork abilities.
Self-motivated, resilient under pressure, and eager to drive business breakthroughs.
