Since 2005, Klarna has been on a mission to reimagine how consumers spend and save in their daily lives. We help people save time and money and reduce financial worry. With over 85 million global active users and 2.5 million transactions per day, our vision is a world where Klarna empowers everyone, everywhere, through seamless financial and commerce experiences—as a personalized, trusted assistant making financial empowerment effortless.
Over 575,000 global retail partners, including H&M, Saks, Sephora, Macys, IKEA, Expedia Group, and Nike have integrated Klarna's innovative technology to deliver a seamless shopping experience online and in-store.
About Us
With over 85 million global active users and 2,5 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'll do
End-to-End Model Ownership : Design, develop, and ship production-grade machine learning models. You will have the autonomy to own your solutions, considering everything from memory footprint to latency.
Research and Innovation : Dive deep into statistical analysis, probability, and machine learning / deep learning models. You'll be expected to critically evaluate existing solutions and, when necessary, develop novel approaches. A foundational understanding of NLP architectures like Transformers is a plus.
Engineering : Build and maintain containerized models and develop comprehensive model monitoring solutions to track metrics like variable drift.
Streamlined Deployment : Create and manage CI / CD pipelines for our machine learning models and contribute to the design and development of APIs.
Data Expertise : Utilize your SQL proficiency to perform in-depth data analysis and feature engineering.
Requirements
Proficient Software Engineering Skills : A strong track record of designing, developing, and deploying ML models in a production environment. Experience with deploying models on Scoring Platform is highly desirable.
Expertise : High proficiency in Python.
Strong Quantitative Foundation : A deep understanding of statistical analysis, probability, and a wide range of machine learning and deep learning models.
MLOps Experience : Hands-on experience with containerization (Docker), CI / CD pipelines, and model monitoring.
API & Containers : Experience in API schema design and development and in building containerized applications (e.g., using Docker)
Excellent communication and stakeholder management skills.
Experience working with consumer or partner Fraud Detection / Credit Underwriting would be a plus.
Ideally, you have :
An academic background in Physics, ML, Data Science, Mathematics, or Biology
Prior experience as a Senior Data Scientist, ideally within a regulated environment or fintech context
Please include a CV in English.
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