Responsibilities
1. Delivery Growth: Through causal inference modeling, user long-term value estimation and other methods, combined with personalized bidding, real-time traffic optimization and other means, to achieve accurate reach of more than one billion users across the entire network, help businesses acquire new users and recall old users with high quality, and use AIGC, multimodal understanding and other technologies to realize automated material production, improve delivery efficiency and effectiveness 2. Incentive growth: For incentive apps such as TikTok Express, Toutiao Express, and Fanqie Novels/Changting, use causal inference, operational optimization and other technologies to design and optimize the numerical strategies of various incentive tasks, improve incentive effects, and optimize marketing fund efficiency. 3. E-commerce growth: Responsible for the optimization of personalized marketing strategies for e-commerce in China, improve the accuracy of personalized pricing through causal inference, deep learning, transfer learning, multi-task learning and other technologies, and apply cutting-edge research results of causal inference and deep learning to provide e-commerce users with more accurate personalized subsidies. 4. Cross-end linkage: Based on ByteDance's APP matrix and traffic pool, combined with recommendations, content generation, incentives and other means, design a reasonable cross-end linkage traffic guidance plan to guide the right users to the right APP at the right time, meet the different needs of users, and bring growth to the ByteDance system as a whole. 5. Intelligent Engine: Build an engineering engine for the efficient implementation of intelligent growth algorithms, design and develop related tools and platforms, support the efficient operation of all links in the entire chain, such as the construction of massive data streams, large-scale model training, high-concurrency online estimation, flexible strategy dynamic adjustment, precise budget control, and reliable capital security.
Qualifications
1. Have a solid foundation in computer science (data structure/algorithm/network, etc.), and have excellent coding skills (at least be proficient in one mainstream development language such as C++/Java/Python) 2. Master the theoretical basis of machine learning, and be familiar with classic algorithm models (GBDT/LR/FM/DNN, etc.) and related tool frameworks (Tensorflow/PyTorch, etc.) 3. Have keen data analysis and insight capabilities, and be familiar with common big data development tools (Spark/Hive/Hadoop, etc.) 4. Have excellent problem-solving skills, good understanding and communication skills, and excellent teamwork awareness, and be passionate, fearless, and willing to challenge 5. Be familiar with causal inference/reinforcement learning/AIGC/CV/NLP and other technologies, and have certain practical experience (optional) 6. Have experience in user growth, intelligent marketing, recommendation, advertising, search algorithms, etc. Practical experience (optional).
