Responsibilities
1. Cooperate with multiple e-commerce business departments such as recommended products/algorithms, search products/algorithms, user growth, e-commerce operations, etc., conduct quantitative analysis through scientific methods, use statistics and machine learning to analyze existing traffic problems, and work with the business to optimize traffic and improve user experience, creating value for users and business 2. Specific contents include: 1) E-commerce indicator system construction: sort out and build an indicator system that affects overall e-commerce traffic and revenue, plan and design business indicator maintenance and data reports, provide decision-making support for business and management, and realize the data productization of traffic business 2) Experiment: conduct in-depth attribution analysis through AB testing, and make directional suggestions for experimental iterations to help experimental iterations and business development 3) In-depth participation in the core business aspects of e-commerce traffic: through regression, prediction, optimization and other modeling methods, explore user behavior patterns, abstract characteristics and models that can be launched online, to optimize business goals, give business suggestions, explore growth points of algorithm-driven business, and promote the implementation of products and technologies 4) Methodological precipitation and innovation: organize relevant teams to conduct training on data product concepts, skills, and tools, and promote digital operations of business departments.
Qualifications
1. Bachelor degree or above, more than 1 year of experience in data analysis, mining, and modeling. Candidates with experience in e-commerce search, recommendation, and advertising data are preferred. Computer, statistics, and mathematics majors are preferred. Candidates with a foundation in machine learning algorithms are given extra points 2. Have the knowledge framework to build a data indicator system, and have experience in building an indicator system master basic data analysis methods, have some experience in implementing data into business, and have the ability to write professional analysis reports 3. Have relatively rich experience in feature engineering, user behavior modeling, etc., and be able to turn abstract problems into concrete models based on actual business scenarios, and ultimately provide data-driven products and insights, and have track records that affect product and business decisions 4. Have experience in data analysis using SQL, R, Python, etc., and those with modeling experience are preferred 5. Passionate about data science, have a sense of ownership, have excellent communication and expression skills, strong ability to withstand pressure, and strong coordination and promotion capabilities.
