At a Glance
- Tasks: Validate credit and finance models, ensuring accuracy and compliance while collaborating with data scientists.
- Company: Join Klarna, a leading fintech company revolutionising the way we manage credit.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on innovation and continuous learning.
- Why this job: Make a real impact in the fintech space by enhancing model validation processes.
- Qualifications: Advanced degree in a quantitative field and 3+ years of relevant experience required.
The predicted salary is between 72000 - 88000 £ per year.
Location: Stockholm; London
Salary: 78293 - 111960 GBP
Type: Full time
Posted: 2026-04-27
Contact: ivy.chiang@klarna.com
What you will do
- Perform independent end-to-end validation of credit risk (e.g., underwriting and limit management), finance (provisioning, offloading, profitability), and other models, rigorously reviewing and challenging all aspects: conceptual soundness, data integrity, feature engineering and selection, training and testing, regulatory compliance and fairness, documentation, deployment, monitoring and business impact.
- Independently replicate the model development process where necessary and conduct challenger analyses.
- Collaborate closely with first-line data scientists, machine learning (ML) engineers, and product stakeholders to understand models’ business context and ensure transparent communication of model risks and validation findings.
- Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and regulatory expectations.
- Drive the continuous enhancement of agentic AI tools that support and accelerate model validation by automating documentation and code review, surfacing cross-source inconsistencies, streamlining challenger analysis, etc.
- Stay up-to-date with emerging trends in credit and finance modelling and AI/ML technologies.
- Maintain robust model risk management frameworks, policies, and procedures in line with evolving regulatory expectations and industry best practices.
Who you are
- Advanced degree (Master’s or PhD) in a quantitative field such as data science, statistics, mathematics, computer science, physics, or engineering; or equivalent experience.
- 3+ years of hands-on experience in credit risk and/or IFRS9/CECL impairment modeling.
- Strong technical expertise in statistical and machine learning models, with a deep understanding of credit risk and/or IFRS9/CECL provisioning models.
- Hands-on experience with programming languages and tools commonly used in data science, such as Python, SQL, Spark, and AWS.
- Excellent analytical, problem-solving, and decision-making abilities.
- A passion for innovation and staying at the forefront of data science and risk management.
- Strong communication and stakeholder management skills, with the ability to convey complex technical information to non-technical audiences.
- Knowledge of regulatory requirements and expectations for model risk management.
Awesome to have
- Experience with Buy Now Pay Later (BNPL), credit cards, personal loans, and payments products.
- Experience mentoring junior validators or leading validation reviews.
- Experience building agentic AI workflows and familiarity with AI governance frameworks and emerging AI regulatory requirements.
Senior/Lead Data Scientist -Credit & Finance Model Validation in London 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/Lead Data Scientist -Credit & Finance Model Validation in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Senior/Lead Data Scientist -Credit & Finance Model Validation in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Klarna, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Klarna. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Klarna
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Klarna!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
