(Senior) Data Scientist
Klarna Bank AB
Berlin, Germany
vor 5 Tg.

Who we are Klarna’s mission is to free people from all the meaningless time spent managing money and purchases, so they can do more of what they love.

Every day at Klarna we help consumers, merchants, and partners to explore just how smoooth the modern purchase experience can be.

Our position at the crossroads of payments, consumer financing, ecommerce and banking means we are uniquely positioned to do this.

There is no label for what we do. Klarna was born in Stockholm in 2005 and today has 2500 employees working across Europe and the US.

We currently serve 60 million consumers, work together with 130,000 merchants and process more than a million payment transactions a day.

We are growing at 40% year on year and our investors include Visa, Atomico, Sequoia Capital, Permira and Bestseller group / Anders Holsch Povlsen.

We have strong partnerships with some of the world’s leading brands, such as ASOS, IKEA, Adidas, Zara, Lufthansa and Spotify.

To find out more about what it's like to work at Klarna : Data Science plays a vital role in Klarna’s operation. Historically, our focus areas have revolved around real-

time risk decision-making for transactions. This can either pertain to credit assessment for individuals or identification of fraudulent transactions.

Since then we have added many new data science areas such predicting customer lifetime value, predicting return behavior in e-

commerce, etc. Data scientists in our Decision Services domain work on both the more mature projects, as well as the newer ones.

As a (Senior) Data Scientist in Decision Services you will :

  • Develop state of the art models to classify transactions and individuals
  • Bring your ideas to production using AWS
  • Be involved in the whole process of model development. This includes everything from root cause analysis, data collection and feature engineering to training, validating and implementing machine learning models, computing performance statistics and live model monitoring
  • Work closely with several engineering teams and business analysts to find new and smart ways of consolidating data and making use of it in order to make better models and ultimately better predictions
  • Be part of the Engineering organisation. Hence your workflow will be software-driven in terms of deploying models to production, using version control, and in general employing software engineering best practices
  • Be innovative, cooperative, collaborative, open and have a flexible mindset with critical thinking
  • In order to be successful in this role we believe that you will have :

  • Strong programming skills in Python, SQL and experience with popular machine / deep learning packages (e.g. scikit-learn, keras, tensorflow, mxnet)
  • Experience with & knowledge of :

  • Cloud platforms such as AWS or Google Cloud;
  • Big data using Hive, Spark, EMR
  • The theoretical foundations of classical and recent machine learning models and algorithms, such as generalized linear models, random forests and ensemble methods, deep neural networks etc.
  • A degree from a university in a highly technically numerate subject (e.g. Maths, Physics, Engineering or Economics).

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