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Senior Data Scientist - Credit Risk: Klarna Card

Sista ansökningsdag
21 september 2024 (20 dagar kvar)
Publiceringsdatum
22 augusti 2024
Område
Yrkesroll
Typ av anställning
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Denna annons är hämtad från Platsbanken / arbetsformedlingen.se

About Us

With over 150 million global active users and 2 million transactions per day, Klarna is on the way to becoming the world’s favorite way to shop. To help us get there, we’re assembling an unparalleled global talent team—accelerating individual careers, and disrupting entire industries. We’re looking for people ready to achieve the extraordinary and embrace our bold ambitions as we shape the future of payments and fintech. Will you join us?


What You Will Do:

  • Lead the development of advanced credit risk models for Klarna Card, focusing on card origination and limit management to ensure continued success across global markets.
  • Enhance underwriting processes and the customer experience by leveraging cutting-edge machine learning techniques.
  • Develop, deploy, and maintain state-of-the-art predictive models, creating robust end-to-end data pipelines within Klarna’s cloud environments.
  • Collaborate with a diverse team of experts from around the globe, bringing your unique perspective to the table and learning from others.

Who You Are:

  • Strong business acumen and product-oriented thinking.
  • Strong communication skills, with the ability to explain technical topics to audiences with varying levels of understanding and collaborate effectively with underwriters and data science colleagues on shared challenges.
  • An academic background in a highly technical, numerate subject (e.g., Mathematics, Physics, Engineering, or Economics).

Awesome to Have:

  • Experience related to credit or fraud risk, portfolio management, banking, finance, model validation, or product analytics.
  • Experience developing classification models with machine learning techniques.
  • Proficiency in Python, SQL, and Git, with an understanding of the concepts behind cloud computing technologies.
  • A deep understanding of the theoretical foundations behind classical and modern machine learning models and algorithms, such as generalized linear models, random forests, ensemble methods, and deep neural networks.


Please include a CV in English.

Ansök nu

Denna annons är hämtad från Platsbanken / arbetsformedlingen.se