Data Scientist -Fraud

Klarna; Warszawa; On-site; PLN 120,000 - 150,000; Full time

Job summary

A leading financial services company in Warsaw is seeking a skilled Data Scientist to build and deploy ML models that protect customers from fraud. The ideal candidate will have an advanced degree in a quantitative field and over 2 years of relevant experience. Proficiency in Python, SQL, and machine learning processes is essential. This role offers an opportunity to work on innovative projects within a dynamic team focused on maintaining high standards of data privacy and risk control.

Qualifications

• 2+ years of experience as a Data Scientist, ML Engineer, or related roles, preferably in the financial sector.
• Proficiency in ML from conceptual design to deployment and monitoring.
• Strong understanding of business value and when to deploy ML solutions.

Responsibilities

• Build and deploy ML models to combat fraud.
• Drive data science projects independently from start to finish.
• Maintain and retrain existing ML models in production.

Skills

Python

SQL

Machine Learning

Data Science

Statistical Analysis

Education

Master or Doctorate in a quantitative field

Tools

AWS (Sagemaker, Lambda, S3, Athena)

Docker

Jenkins

ML modeling packages (e.g. scikit-learn, LGBM)

GitHub

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.