Data Science, Analytics - Data Mining Intern

  • Company: LinkedIn
  • Posted: December 17, 2016
  • Reference ID: 1042425146
Data scientists on the Analytics team at LinkedIn help ensure that we use data to make product decisions wisely, taking into account our members multi-screen usage of LinkedIn at various times throughout their professional careers, and in determining the best ways to engage users, optimize member growth, and increase monetization.
LinkedIn's amazingly rich data about our worldwide network of professionals is a fantastic playground for a Data Scientist. You'll have the opportunity to work with some of the best data people anywhere in an environment which truly values (read: is obsessed with) data-driven decisions.
Responsibilities: Analyze and mash-up massive amounts of data to mine useful business insights; Develop predictive models to understand member behavior and impact LinkedIn product/marketing/sales; Contribute to LinkedIn's experimentation/predictive modelling ecosystem; Build scalable backend solutions for automation of data processing; Develop presentation stories and slides based on the analysis.
Basic Qualifications: Must be currently enrolled in a college or university program and returning to the program after the completion of the internship; Currently pursuing a MS/PhD in a quantitative discipline: statistics, applied mathematics, operations research, computer science, management of information systems, engineering, economics, social sciences or equivalent; Experience with R or other statistical packages for clustering, classification, and text mining Experience with Unix/Linux environment for automating processes with shell scripting Experience with relational databases and SQL; Experience with data mining, machine learning, econometric analysis or equivalent.
Preferred Qualifications: Experience in Hadoop or other MapReduce paradigms and associated languages such as Pig and Hive; Front-end development experience Programming skills with Python and Java & interest in learning new ones; Proficiency in a Unix/Linux environment for automating processes with shell scripting; Previous experience and/or advanced coursework in modelling, experiment design or survey research; Ability to communicate findings clearly to both technical and non-technical audiences; Being self-motivated, creative and collaborative; Strong presentation and communications skills.

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