At a Glance
- Tasks: Lead the design of real-time credit scoring models and mentor fellow data scientists.
- Company: Join Klarna, a leader in consumer credit innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with diverse teams and exciting challenges.
- Why this job: Make a significant impact on consumer lending with cutting-edge machine learning techniques.
- Qualifications: 5+ years in data science, expertise in Python, SQL, and ML.
The predicted salary is between 60000 - 80000 £ per year.
Klarna is seeking a Lead Data Scientist to enhance their consumer credit scoring and portfolio valuation models. You will work on designing real-time PD models and integrating them into the underwriting framework.
The ideal candidate has over 5 years of experience, particularly in consumer lending. This role involves collaborating with varied teams to translate complex modeling insights into tangible business strategy while also mentoring peers.
Proficiency in Python, SQL, and advanced ML techniques is essential.
Lead Credit Risk Scientist — Real-Time Scoring & ML employer: Klarna
Klarna is an exceptional employer that fosters a collaborative and innovative work culture, where your expertise in data science will directly impact consumer lending strategies. With a strong emphasis on employee growth, you will have access to mentorship opportunities and cutting-edge projects in a dynamic environment located at the heart of a thriving tech hub. Join us to be part of a forward-thinking team that values creativity and offers competitive benefits, making it a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Credit Risk Scientist — Real-Time Scoring & ML
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Klarna!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Credit Risk Scientist — Real-Time Scoring & ML at Klarna.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Klarna.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Credit Risk Scientist — Real-Time Scoring & ML at Klarna, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Credit Risk Scientist — Real-Time Scoring & ML
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.
