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Data Scientist - Fraud Prevention

Creata il 21-05-2018
Location Tallinn

Descrizione

What it’s really like to work here


At TransferWise, we do things a bit differently. There’s no corporate nonsense, and no old-fashioned hierarchy. Instead, we work in dozens of self-sufficient, autonomous teams. Think of them like start-ups within a start-up.


Each team picks the problems they want to solve. So there’s no micro-management. No hiding behind fancy job titles. And no one telling you what to do. You are your own boss. But you’ll get tons of guidance and plenty of support from your talented, super-smart teammates.  


We’re going to be upfront — the way we work doesn’t suit everyone. But if freedom, autonomy, and life-affirming, head-scratching professional challenges rock your world, we could be a match made in heaven.


About the role


TransferWise has become a global platform to transfer money, which hosts millions of transactions every month. At this scale, it is extremely crucial to detect and prevent fraudulent activity amongst those transactions in real time. The Fraud Team is developing scalable solutions to this sophisticated problem in a small autonomous setting. If you like the idea of analyzing fast moving data, building continually learning systems and using cutting-edge tools to develop processes that all focus on helping our million customers then we will love to speak with you!


As a Data Scientist in the Fraud Team you will be expected to:


  • Develop features and algorithms from our rich data that are critical to our models.
  • Research, design, implement and iterate on machine learning models to detect fraudulent activity in real time.
  • Collaborate with a strong team of developers, product leads and fellow data scientists to build a fraud product that is constantly evolving to catch new trends in a scalable manner.
  • Determine strong metrics to evaluate the state of our models' effectiveness.
  • Ensure data quality throughout all stages of the modeling process and contribute to our data pipeline.
  • Put the customer at the heart of everything you do and that empathy drives every decision you make.
  • Ability to work independently and plan your own solutions to problems.

Key Skills, Experience and Knowledge:


  • Experience in developing machine learning models from the ideation stage to solving a business case.
  • Experience working with raw data, preprocessing and feature engineering.
  • A deep understanding of widely used ML techniques for classification problems and why a particular model is performing or not performing as expected.
  • Strong programming experience (Python is a must).
  • Experience with distributed computing environment (Spark) and cloud computing platforms (AWS) is preferred.
  • Experience collaborating with engineers and ability to explain complex statistical models and analysis to drive customer impact.

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Benefit

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