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Criteo: Senior Data Scientist – Machine Learning


Outstanding machine learning research scientists to contribute to Criteo in driving the future of ad targeting, personalization, content extraction, content matching and other prediction problems.



Criteo Company: Criteo
Location: Palo Alto, CA or Paris, France
Web: www.criteo.com


About CRITEO

Criteo is a leading global technology company that specializes in performance display advertising, working with over 4,000 ecommerce companies around the world. Criteo has over 1200 employees in offices across the U.S., Europe and Asia serving more than 35 countries.

For more information, please visit www.criteo.com

Overview

Criteo Labs is pioneering the new sciences underlying online publishing and advertising across all devices - desktop, smartphones, and tablets. As the center of scientific excellence for Criteo, Criteo Labs in Palo Alto delivers both fundamental and applied scientific leadership through published research, product innovations and new technologies powering the company's products.

We are looking for outstanding machine learning research scientists whose skills span the entire spectrum of Machine Learning/Data-Science research, i.e. data-gathering/cleaning, modeling, implementation, publication and presentation. In particular we are seeking researchers with the following skills and experience to contribute to Criteo in driving the future of ad targeting, personalization, content extraction, content matching and other prediction problems.

Your mission:
  • Click prediction: How do you accurately predict if the user will click on an ad in less than a millisecond? Thankfully, you have billions of data points to help you.
  • Recommender systems: A standard SVD works well. But what happens when you have to choose the top products amongst hundreds of thousands for every user, 2 billion times per day, in less than 50ms?
  • Auction theory: In a second-price auction, the theoretical optimal is to bid the expected value. But what happens when you run 15 billion auctions per day against the same competitors?
  • Explore/exploit: It's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm?
  • Offline testing: You can always compute the classification error on model predicting the probability of a click. But is this really related to the online performance of a new model?
  • Optimization: Stochastic gradient descent is great when you have lots of data. But what do you do when all data are not equal and you must distribute the learning over several hundred nodes?

 
Are you interested in tackling such problems in an environment where your algorithms are deployed by a team that sits next to you?

Role:
  • Gather and analyze data, identify key prediction/classification problems, devise solutions and build prototypes
  • Contribute to the exploration and creation of new scientific understanding
  • Initiate and propose unique and promising modeling projects, develop new and innovative algorithms and technologies, pursuing patents where appropriate
  • Stay current on published state-of-the-art algorithms and competing technologies
  • Maintain world-class academic credentials through publications, presentations, external collaborations and service to the research community
  • Develop high-performance algorithms for precision targeting, test and implement the algorithms in scalable, product-ready code
  • Research and investigate academic and industrial data mining, machine learning and modeling techniques to apply to our specific business cases
  • Interact with other teams to define interfaces and understand and resolve dependencies
  • Participate in academic conferences and publish research papers

 
Qualifications:
  • PhD in Statistics, Machine Learning or a related field, with a previous major in Computer Science
  • Experience with traditional as well as modern statistical learning techniques, including: Support Vector Machines; Regularization Techniques; Boosting, Random Forests, and other Ensemble Methods
  • Strong implementation experience with high-level languages, such as R, Python, Perl, Ruby, Scala or similar scripting languages
  • Familiarity with Linux/Unix/Shell environments
  • Strong hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data
  • Experience with end-to-end modeling projects emerging from research efforts
  • Excellent academic or industrial track record of proposing, conducting and reporting results of original research, plus collaborative research with publications
  • Strong communication skills both written and oral
  • Knowledge of Hadoop programming environments (e.g. Pig, Hive)

 
_Contact_:
Please send resumes to Ann Livingood at a.livingood@criteo.com

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