Data Scientist
Kenshoo Tel Aviv, Israel Competitive Long term Any
About Kenshoo
       Kenshoo is a digital marketing technology company that engineers premium solutions for search marketing, social media and online advertising. Kenshoo's mission is to empower every marketer in the world with technology to build brands and generate demand across all media. Brands, agencies and developers use Kenshoo Enterprise, Kenshoo Local and Kenshoo Social to direct more than $25 billion in annual client sales revenue.
Job Description

       Kenshoo Research creates algorithms that bring real value to the world’s top advertisers. Its role consists of identifying and defining functionality from conception to delivery, by fully understanding customer requirements, data, ‘connecting the dots’, creating an algorithm, and delivering a working algorithm to production.

       Role Description:

       Researching, selecting and tuning machine learning models and algorithms to solve real world business problems using Kenshoo’s data: internal and 3rd party user interaction tracking (big data), publisher (Google, Facebook, Bing etc.) data and other industry data. Creating and improving algorithm components, specifically the algorithm code itself. Working with a team of data scientists that help each other to mine insights and find opportunities in interesting data from a variety of fields and geographies. Working with the algorithm developers team to integrate these algorithms with the production data sources and the application ecosystem. Working with data analysts to assess the algorithm quality and performance and find areas for improvement.


Requirements

  • Msc or PhD in Computer Science, Mathematics, Statistics, Physics or related fields.
  • Extensive experience with data mining / machine learning / optimization.
  • Experience in one or more of the following areas is an advantage:
  • Marketing (Search Engine/Display/Social) auctioning and bidding algorithms - big advantage!
  • Deep learning.
  • Scalable Classification and Clustering techniques.
  • Statistical modeling / information gains.
  • Java / Scala
  • Big data technologies - Hadoop, MapReduce, Hive, Spark.
  • Python / R data science / analysis libraries.


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