14 hours agoLow ghost risk
KlarnaOn-siteStockholm, ME$120K - 180KLead
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- $166K median pay
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Why this job matches
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Summary
Klarna is seeking a Senior/Lead Data Scientist to develop and maintain advanced consumer-level credit scoring and portfolio valuation models. This role focuses on designing real-time Probability of Default models, ensuring regulatory compliance, and driving strategic credit policies through data-driven insights.
Responsibilities (What you'll do)
- Design and maintain real-time Probability of Default (PD) models using statistical and machine learning approaches.
- Integrate credit risk models into underwriting and economic return optimization frameworks.
- Develop calibration frameworks and ensure models comply with regulatory and fairness standards.
- Explore and implement novel methodologies, including large language models (LLMs) for explainability and feature engineering.
- Collaborate with cross-functional teams to translate modeling insights into strategic credit policies and business value.
- Mentor junior team members and contribute to the long-term modeling vision at Klarna.
Qualifications (What skills you have to have)
- 5+ years of experience in credit risk modeling for consumer lending, credit cards, or Buy Now Pay Later (BNPL).
- Deep proficiency in Probability of Default model development and validation, including calibration techniques.
- Advanced skills in Python and SQL, with experience using XGBoost, scikit-learn, pandas, and MLFlow.
- Experience with explainability frameworks such as SHAP, LIME, and PDP.
- Ability to communicate technical concepts clearly and influence cross-functional decisions.
- Familiarity with real-time modeling and current trends in machine learning and credit analytics.
- Preferred: Hands-on experience using LLMs to extract features from unstructured data.
- Preferred: Knowledge of integrating third-party credit bureau data into production models.
- Preferred: Understanding of champion/challenger model frameworks and A/B testing infrastructure.
- Preferred: Exposure to loan-level economic modeling, including cost-of-capital and loss metrics.
Company Reviews
Employee reviews3.1
2,090 Reviews- Enjoyed my time here2026-03-08T09:47:18.773
Pros: For analysts, the tech setup was very robust. I enjoyed learning new techniques and for a chance to reasonably impact the strategy of new product launches. There were also set templates on how to share your work and structure meetings, which I thought helped reduce chaos.
Cons: I left for personal qualms around working on BNPL lending. The company itself was great to work at and I do miss it.
- Was great, not so much anymore2026-03-03T06:47:55.820
Pros: Great colleagues, cool product, international experience
Cons: Pay does not keep up with market, C-suite have unrealistic expectations.
- Decent company but long hours for average pay2026-02-20T05:18:25.483
Pros: good opportunities with a product that keep groing
Cons: Somewhat toxic work environment, long hours.
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Frequently asked questions about lead data scientist jobs
What is a ghost job?
A ghost job is a posting for a role the company isn't actively hiring for. Some stay up after the company fills the role. Others collect resumes for an opening that may never come.
You spend an evening tailoring an application, and nobody on the other side is looking for you.
What do lead data scientist jobs pay?
Across the postings here that list pay, the median is about $166,000 a year. About 5 in 10 of these postings list a pay range; the rest show an estimate, marked as one.
How many lead data scientist jobs are there?
At our last count there were about 110 open postings. The list on this page is live, so the count above it is the current one.
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