Senior/Lead Data Scientist- Open Banking

Klarna; Stockholms kommun; On-site; SEK 1,100,000 - 1,500,000; Full time

Job summary

Klarna is seeking a Senior Data Scientist in Open Banking to turn raw bank data into actionable insights for fraud prevention and underwriting decisions. You will own the full ML lifecycle, from data to production deployment, and work with engineering to scale your models.

You will craft rich features from transactional data, build models to estimate default probabilities and fraud scores, and monitor performance via dashboards in collaboration with Credit Modeling and Fraud teams.

Qualifications

• Experience building and deploying ML models end to end in production.
• Strong feature engineering, preferably with transactional/financial data.
• Proficient in Python and SQL; experience with AWS environments.
• Solid understanding of classification models for risk or fraud.

Responsibilities

• Build and deploy ML models end-to-end from raw data to production.
• Engineer features from transactional data to support affordability and risk models.
• Develop dashboards to monitor model performance and impact.
• Collaborate with Credit Modeling and Fraud teams to align goals.
• Partner with engineering to operationalize models at scale.

Skills

End-to-end ML deployment

Feature engineering

Production collaboration

Communication

Cross-functional teamwork

Ownership mindset

Tools

Python

SQL

AWS

Databricks

Datadog

Job description

What You Will Do

As a Senior Data Scientist within our Open Banking team, you will build the analytical foundation that turns raw bank account data into reliable, actionable insight for fraud prevention and underwriting decisions. You will engage in feature engineering across incoming and outgoing cash flows to model affordability, and develop machine learning models that estimate probability of default and produce fraud scorecards. You will own the full lifecycle of these models, from raw data through to production deployment, working closely with engineering to operationalize your work at scale. You will also help monitor model performance, impact, and acceptance rates through dashboards you help build, and you will partner closely with the Credit Modeling and Fraud teams to align on shared goals and infrastructure.

Who You Are

• Experience building and deploying machine learning models end to end, from raw data to production
• Strong feature engineering skills, ideally with transactional or financial data
• Proficient in Python and SQL, with experience working in AWS environments
• Solid understanding of classification modeling techniques for risk or fraud use cases
• Comfortable partnering closely with engineering teams to bring models into production
• Strong communication skills, with the ability to work cross-functionally with Credit Modeling and Fraud teams
• A curious, ownership-driven mindset, comfortable building infrastructure and processes from scratch

Awesome to have

• Experience with open banking data or affordability modeling
• Familiarity with building dashboards for model monitoring, ideally with tools like Databricks
• Experience with monitoring and observability tools such as Datadog
• Background in underwriting, credit risk, or fraud detection use cases