Remote Data Scientist

Varwise

Lisbon, Portugal

há 5 dias

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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