Data Scientist -Fraud

Klarna; Stockholms kommun; On-site; SEK 600 000 - 800 000; Full time

Job summary

A leading financial technology company in Stockholm is seeking a Data Scientist to build and deploy machine learning models to combat fraud. The ideal candidate will have an advanced degree in a quantitative field and over 2 years of relevant experience. You will be responsible for driving data science projects and must possess strong Python and SQL skills. The position requires excellent communication abilities and the willingness to collaborate across global teams.

Qualifications

• 2+ years of experience as a Data Scientist or related role in the financial sector.
• Good understanding of business value in deploying ML solutions.
• Ability to communicate effectively with technical and non-technical roles.

Responsibilities

• Build and deploy ML models to protect customers from fraud.
• Independently drive data science projects from problem definition to deployment.
• Monitor and maintain existing ML models in production.

Skills

Python

SQL

ML end-to-end process

Data Science

Stakeholder communication

Education

Advanced degree (Master or Doctorate) in a quantitative field

Tools

scikit-learn

Docker

Jenkins

AWS Cloud Computing

Job description

What you will do

• Build and deploy ML models to protect Klarna’s customers from fraudulent activities (e.g. account takeover or identity theft fraud).

• Independently drive data science projects, from problem definition until deployment.

• Monitor, maintain, and retrain existing ML models in production.

• Explore, engineer, and test new potential features to predict fraud or increase conversion.

• Communicate with stakeholders on conceptual design, development, deployment, and risk control of the model, including writing documentation for external parties.

• Maintain the engineering platform/system used by the team to stay compliant with the company’s requirements.

• Explore novel ML/AI solutions to detect fraud.

Who you are

• Have an advanced degree (Master or Doctorate) in a quantitative field (e.g. statistics, computer science, engineering, mathematics, physics, or related fields).

• 2+ years of experience as a Data Scientist, ML Engineer, or related roles, preferably in the financial sector.

• Proficiency in ML end-to-end process from conceptual design to model development, deployment, and monitoring.

• Good understanding of business value to deliver: know when an ML solution is needed and when the model is good enough to be deployed for production.

• Good understanding of what metrics to use for model monitoring.

• Strong Python and SQL skills, including familiarity with ML modeling packages (e.g. scikit-klean, LGBM) and CI/CD or deployment tools (e.g. Docker, Jenkins, and uv).

• Familiarity with Github and AWS Cloud Computing (Sagemaker, Lambda, S3, Athena, etc).

• Ability to communicate effectively with Analysts, Engineers, and non-technical roles.

• Willingness to collaborate across different locations and time-zones (US EU), but you will be working at common office hours in your time-zone. Traveling for one or two weeks per year may be needed to meet in-person with other group members.

• Willingness to take ownership of a project and deliver results with minimal supervision.

• Agile to adapt to new changes in technology or engineering platforms used by the company.

Awesome to have

• Experience working in fraud-related problem space, cyber security, and/or payment-related business, e.g. BNPL, credit card, or P2P transfer.

• Experience in handling large sizes of customer data (>100 millions transactions with a few hundred features).

• Technical experience on utilizing Gen AI, Graph Network, Anomaly Detection, or Behavioral Biometrics into production (beyond just prompting, fine-tuning, or proto-typing solutions).

• Familiarity with AI productivity tools for coding, e.g. Cursor or Github co-pilot.

• Familiarity with compliance and regulation around personal data privacy and model bias.

• Experience in mentoring junior data scientists.