At a Glance
- Tasks: Lead the development of innovative credit scoring models and collaborate with diverse teams.
- Company: Join Klarna, a forward-thinking company revolutionising consumer credit.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on cutting-edge methodologies and career advancement.
- Why this job: Make a real impact on credit risk modeling while mentoring future data scientists.
- Qualifications: 5+ years in credit risk modeling with strong Python and SQL skills.
The predicted salary is between 54000 - 84000 £ per year.
As 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 Klarna’s long‑term modeling vision.
Who You Are
- 5+ 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.
Senior Data Scientist - Credit employer: Klarna
Klarna is an exceptional employer that fosters a dynamic and innovative work culture, perfect for those passionate about fintech and machine learning. With a focus on employee growth, you will have access to cutting-edge projects and the opportunity to make a tangible impact within a collaborative team environment. Located in a vibrant tech hub, Klarna offers unique advantages such as flexible working arrangements and a commitment to professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist - Credit
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Klarna. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your previous work in credit risk modeling. Use real examples of PD models you've developed or any innovative methodologies you've explored—this will make you stand out.
✨Tip Number 3
Practice makes perfect! Brush up on your Python and SQL skills, and get comfortable with tools like XGBoost and scikit-learn. Being able to demonstrate your technical prowess during interviews is key.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team at Klarna.
We think you need these skills to ace Senior Data Scientist - Credit
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Senior Data Scientist role. Highlight your experience in credit risk modeling and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Showcase Your Skills:Don’t just list your skills—show us how you’ve used them! Include specific examples of your work with PD models, Python, SQL, and any ML frameworks. This helps us understand your hands-on experience and technical prowess.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about credit risk modeling and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of our mission.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Klarna
✨Know Your Models Inside Out
Make sure you can discuss your experience with PD model development and validation in detail. Be ready to explain the calibration techniques you've used and how they apply to real-time modelling. This will show your depth of knowledge and confidence in your expertise.
✨Showcase Your Technical Skills
Brush up on your Python and SQL skills before the interview. Be prepared to discuss specific projects where you’ve used libraries like XGBoost or scikit-learn. If you can, bring examples of your work that demonstrate your proficiency and problem-solving abilities.
✨Communicate Clearly
Since you'll be collaborating with cross-functional teams, practice explaining complex technical concepts in simple terms. Think about how you can influence decisions with your insights and be ready to share examples of how you've done this in the past.
✨Stay Updated on Trends
Familiarise yourself with current trends in machine learning and credit analytics, especially around explainability frameworks like SHAP and LIME. Being able to discuss these topics will show that you're not just experienced but also forward-thinking and engaged with the industry.
