JD.com is China’s largest online retailer and its biggest overall retailer, as well as the country’s biggest Internet company by revenue. JD.com sets the standard for online shopping through its commitment to quality, authenticity, and its vast product offering covering everything from fresh food and apparel to electronics and cosmetics. Its unrivalled nationwide fulfillment network provides standard same- and next-day delivery covering a population of more than 1 billion - a level of service and speed that is unmatched globally. In 2020, JD had 500M+ annual active consumers and net revenue of US$114.3B. JD.com is listed on the NASDAQ under the ticker “JD”. In 2014, the year of its listing, this was the largest IPO on the NASDAQ. JD.com is a member of the Fortune Global 500 List. 

The ideal candidate for this position will be quantitatively trained (advanced master's or PhD) with expertise in quantitative and econometric methodologies, and with a demonstrable understanding of causal inference methods. Individuals who are interested in more deeply understanding consumer behavior, "firm" behavior, and competition and who have a passion for solving complex business problems through the combined use of data, technology and strategy will thrive in our environment.  They will also be comfortable working cross-functionally and thrive in a fast-paced organization. Interest in e-commerce and in economics/quantitative marketing/business-analytics is a plus.


  • Help suggest, support and shape new data-driven advertising, e-commerce, and marketing products
  • Collaborate with teams to define relevant questions about user behavior, brand safety, user demand, advertising effectiveness, targeting, pricing, promotions, and develop and implement quantitative methods to answer those questions
  • Find ways to combine large-scale experimentation, statistical-econometric, machine learning and social-science methods to answer business questions at scale
  • Use causal inference methods to design and suggest experiments and new ways to establish causality, assess attribution and answer strategic questions using data
  • Help product leaders crystallize research into strategic decisions and communicate them to high level audiences


  • Fluency in data manipulation and analysis (must know SQL/Hive/Pig; expert-level knowledge of at least one of R/Matlab/Python; familiarity with infrastructure for Big Data: Hadoop, MapReduce a plus)
  • Training and experience in using advanced quantitative methods (statistics, econometrics, ML, RL, causal inference, experimentation)
  • Must understand potential outcomes framework and have familiarity with causal inference methods such as split-testing, instrumental variables, difference-in-difference methods, fixed effects regression, panel data models, regression discontinuity, matching estimators. Knowledge of structural econometric methods is a plus.
  • Experience with large datasets and interest in consumer behavior, e-commerce and related business questions
  • Ph.D in a quantitative, data-analytic field, especially economics, marketing and/or statistics and related disciplines is a must
  • Senior/Principal roles require minimum 2 years or more experience in a data-intensive role

About JD-Technology and Data Center

The JD – Technology and Data Center is part of JD Retail. The Technology and Data Center develops technologies and data solutions that powers JD’s retail business. JD Retail Technology research group in Silicon Valley is focused on using science to suggest, support and shape new and existing data-driven promotion, e-commerce, and marketing products.  We combine large-scale experimentation, statistical-econometric, machine learning and artificial intelligence tools with social-science, economics and computer science to find innovative ways to drive growth and monetization. Some of the problems we work on include causal evaluation, recommendation systems, targeting and pricing using applied economics, causal inference, deep learning and reinforcement learning methods.

JD.com is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class.

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