Remote Data Scientist
Ostrava, Czech Republic
před 2 dny
source : Just Join IT

Golang (nice to have)

Scala (junior)

scikit-learn (junior)

R (regular)

TensorFlow (advanced)

Python (advanced)

We are looking for Senior Data Scientists to work remotely on a data science projects that form a RTB exchange. The Exchange operates over 100k requests / s and all teams constantly face with high volume, low latency and geo-distributed challenges.

Our data volume is over 20TB / day, over 200 uncompressed.

This is a 100% remote position, for qualified candidates we help with visa and relocation.

In this role you will be working on the conception, design, development, testing, and deployment of real-world applications of models & data that impact billions of dollars of marketing spend.

From optimizing real-time bidding auctions, to separating human from non-human web traffic, to building out a global cross-device graph across billions of users, you will have the opportunity to work on numerous cutting-edge problems and develop scalable, high-performance solutions to big data problems using state-of-the art technologies, languages and frameworks.

Key Requirements :

  • BS, Masters, or PhD degree with 5+ years of industry experience in a quantitative discipline
  • Experience in implementing projects in Tensorflow (preferred) or scikit-learn or Pytorch.
  • Solid understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests
  • Experience with data visualization tools, such as for example D3.js
  • Practical programming experience (for example Python / Scala or Golang)
  • Previously participated in deploying Machine Learning Models all the way from research to production pipeline
  • Role Details :

  • Design and develop Machine Learning models and algorithms
  • Partner closely with Engineering on the architecture and implementation of modeling efforts to ensure performance and scalability
  • Work seamlessly with Product and Analytics to define concrete measures of success, including business level goals as well as improvement of outcomes
  • Develop tools and processes to monitor performance of existing models and implement enhancements to improve scalability, reliability, and performance
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