Senior/Lead Data Scientist -Credit Risk Modeling London £81,425 GBP - £116,438 GBP

Klarna
London, UK
9 days ago
Apply on www.apply4u.co.uk
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
£81,425.0 - £116,438.0
Working hours
Regular working hours

Tech stack

A/B Testing Python (Programming Language) Standard Sql Unstructured Data Feature Engineering Large Language Models Pandas Scikit Learn Xgboost

Job description

At Klarna, our credit risk models sit at the heart of how we underwrite and price risk for millions of consumers globally.We’re looking for a Lead Data Scientist to help shape the next generation of consumer-level credit scoring and portfolio valuation models.What you’ll doAs a Lead Data Scientist within credit risk modeling, you will shape Klarna’s next-generation consumer-level credit scoring and portfolio valuation models. You’ll design and maintain real-time PD (Probability of Default) models using statistical and ML approaches, integrating them into frameworks for underwriting and economic return optimization. You’ll develop calibration frameworks, ensure compliance with regulatory and fairness standards, and explore novel methodologies - including LLMs for explainability and feature engineering. Collaborating with cross-functional teams, you’ll translate modeling insights into strategic credit policies and business value, while mentoring junior team members and contributing to

Requirements

Klarna’s long-term modeling vision.Who you are5+ years’ experience in credit risk modeling for consumer lending, credit cards, or BNPL.Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques.Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow.Experience with explainability frameworks such as SHAP, LIME, PDP.Ability to communicate technical concepts clearly and influence cross-functional decisions.Familiarity with real-time modeling and current trends in ML and credit analytics.Awesome to have Hands-on experience using LLMs to extract features from unstructured data (e.g., customer communications, credit applications).Knowledge of integrating third-party credit bureau data into production models.Understanding of champion/challenger model frameworks and A/B testing infrastructure.Exposure to loan-level economic modeling, including cost-of-capital and loss metrics.Curious to learn more about Klarna and what it’s like to work here? #J-18808-Ljbffr

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.apply4u.co.uk
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:03 min

Accelerating pandas dataframes using cudf module plugins

Ankit Patel Ankit Patel · World Congress 2024

1:09 min

Configuring synthetic data for safe interactive programming

Mingshen Sun Mingshen Sun · World Congress 2024

54 sec

Generating multiple hook options for outreach A/B testing

Leandro Gomes da Silva Leandro Gomes da Silva · World Congress 2025

2:39 min

Defining rules and constraints for credit card fraud validation

Tim Faulkes · LIVE

6:58 min

Analyzing production code coverage data using pandas

Markus Harrer Markus Harrer · World Congress 2021

5:21 min

Preventing customer churn with predictive machine learning models

Andreas Christian · LIVE

Videos

See all

Related articles

See all