Our data scientists and researchers work to unlock the potential in our data to change the world by empowering professionals to become more productive and successful.

LinkedIn operates the world’s largest professional network on the Internet with more than 380 million members in over 200 countries and territories. This highly structured dataset gives our data scientists and researchers the ability to conduct applied research that fuel LinkedIn’s data driven products including search, social graph, and machine learning systems. As a members first organization, LinkedIn keeps the privacy and security of our members at the forefront in all our research.  

LinkedIn’s team of data scientists and researchers work with huge amounts of data, solve real problems for our members around the world and publish at major conferences. They work to improve the relevance in our products, contribute to the open source community and actively pursuing research in a number of areas, including:

  • Computational advertising
  • Data & graph mining
  • Machine learning & infrastructure
  • Recommender systems
  • Online experimentation and A/B testing
  • Text mining and sentiment analysis
  • Machine translation, cross language text analysis
  • Security & SPAM
  • Information extraction
  • Content understanding
  • Scalable computing paradigms (M/R, Spark, etc.)
  • Network visualization



Many of our Talent Solutions products are built around mathematical models that try answer a simple question: is this opportunity of interest to this member at this time? Answering that question requires a multidisciplinary approach, drawing on tools from machine learning and data mining and utilizing insights from psychological and sociological research.


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We built our internal end-to-end A/B testing platform, called XLNT, to quickly quantify the impact of any A/B test in a scientific and controlled manner across our sites and mobile apps. XLNT allows for easy design and deployment of experiments, but it also provides automatic analysis that is crucial in popularizing A/B tests.



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From catching up on trending news, getting updates from your network, or following a thought leader from ourInfluencer program – the professional information and insights in the LinkedIn Feed have become central to our member experience.





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The NLP team provides natural language processing (NLP) tools, analyses and features for use throughout the entire company. Our mission is to take unstructured text, analyze it along with information from our structured and semi-structured sources, and produce useful structured representations for all of LinkedIn's current and future product areas. 



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Featured publications

From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks

Authors: Ya Xu, Nanyu Chen, Adrian Fernandez, Omar Sinno, Anmol Bhasin


Published:21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015)


Abstract: A/B testing, also known as bucket testing, split testing, or controlled experiment, is a standard way to evaluate user engagement or satisfaction from a new service, feature, or product.

Personalizing LinkedIn Feed

Authors: Deepak Agarwal, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, Liang Zhang


Published: 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015)


Abstract: LinkedIn dynamically delivers update activities from a user’s interpersonal network to more than 300 million members in the personalized feed that ranks activities according their “relevance” to the user.