Applied Scientist, Recommendation, E-Commerce Alliance

The e-commerce alliance team aims to serve merchants and creators in the e-commerce platform to meet merchants' business indicators and improve creators' creative efficiency. By cooperating with merchants and creators, we aim to provide high-quality content and a personalized shopping experience for TikTok users, create efficient shopping tools at seller centers, and promote cooperation between merchants and creators. We are actively seeking an Applied Scientist to join our Global E-Commerce Alliance Team. This role is centered on developing and implementing innovative machine learning solutions for our recommendation systems in E-Commerce business. The successful candidate will work closely with cross-functional teams, providing expert insight and influencing critical decision-making across multiple areas of our business. Responsibilities: - Collaborate with cross-functional teams to design, develop, and deploy sophisticated machine learning algorithms to enhance the performance of our recommendation systems. - Utilize the ML, NLP, and CV techniques to deal with real-world signals generated from products, creators, merchants, e-commerce transactions, and so on. - Design and deploy the large recommendation model, in the online learning manner, to serve billions of queries and products. - Formulate end-to-end machine learning models for recommendation systems, ensuring their efficient and effective operation. - Analyze extensive, complex datasets to extract meaningful insights, identify opportunities for improvement, and facilitate data-driven decision-making. - Design and execute experiments, testing and iterating on machine learning models to optimize recommendation functions and boost user satisfaction. - Stay abreast of the latest advances in machine learning and recommendation systems, integrating this knowledge into your work. - Clearly communicate complex technical concepts, methodologies, and results to a diverse audience, influencing decisions based on your findings. - Adhere to stringent data governance and privacy protocols, ensuring all user data is handled responsibly and ethically. Minimum Qualifications - PhD or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline. - Solid experience in machine learning, deep learning, data mining, or artificial intelligence. - Proficient in programming languages such as Python, C++, Java, or similar. - Deep understanding of recommendation algorithms and personalization systems. - Excellent problem-solving and analytical skills. - Strong ability to communicate complex ideas effectively to both technical and non-technical audiences. Preferred Qualifications - Experience with reinforcement learning techniques. - Proven modeling/algorithms competition records on Kaggle or top conferences’ challenges. - Proven programming competition records on ICPC, IOI or USACO. - Experience working with recommendation systems, computational advertising, search engine, E-commerce recommendation systems. - Publications in machine learning or related conferences or journals are highly desirable.