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 2000 employees working across Europe and the US. We currently serve 60 million consumers, work together with 90,000 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: klarna.com/careers
The Fraud Modelling team at Klarna has created a Fraud Detection Platform with Machine Learning at its core to identify and reject fraudulent transactions in real-time. We are looking for a (senior) data scientist to strengthen the team.
What will you work on?
- Develop state of the art supervised and unsupervised machine/deep learning models models to identify fraudulent transactions
- Bring your ideas to production using AWS
- Reduce reliance on hand-crafted rules in decision making
- Augment the workflow of fraud agents with machine learning models to improve fraud detection performance
- As we are part of engineering, your workflow will be software-driven in terms of deploying models to production, using version control, and in general employing software engineering best practices
What will you need to be successful in this role?
- Strong programming skills in Python, SQL and knowledge of popular machine/deep learning packages
(e.g. scikit-learn, keras, tensorflow, mxnet)
- Hands-on experience with cloud platforms such as AWS or Google Cloud
- Experience working with big data using Hive, Spark, EMR
- Hands-on experience and knowledge of the theoretical foundations with classic and recent machine learning models and algorithms, such as generalised linear models, random forests and ensemble methods, deep neural networks etc.
- 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 innovative, cooperative, collaborative, open and have a flexible mindset with critical thinking
- A degree from a university in a highly technically numerate subject (e.g. Maths, Physics, Engineering or Economics)
- Bonus points if you have experience in fraud detection using GraphDB or unsupervised learning techniques such as clustering, autoencoders, anomaly detection etc.
What you can expect from us
- A generous compensation package
- 30 days of paid holidays per year
- Pension and insurance plans
- Budget for personal development, including books, conferences and workshops
- Massive potential to develop and grow, both personally and professionally
How to apply
Send over a CV in English.
We can offer you an international working environment filled with smart and ambitious colleagues. We know that diverse teams are strong teams, so we welcome those from different backgrounds and experiences. As part of one of Europe’s fastest growing companies, you'll help play an important role in taking Klarna to the next level.