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- Tasks: Build automated credit strategies for SMEs using data analysis skills.
- Company: Dynamic European fintech company based in London.
- Benefits: Flexible working conditions and opportunities for cross-functional projects.
- Why this job: Make a real impact on credit risk strategies in a data-driven environment.
- Qualifications: Strong data analysis skills with SQL and Python experience.
The predicted salary is between 36000 - 60000 £ per year.
A European fintech company based in London is seeking a Credit Risk Analyst – SME to build automated credit strategies for small and medium-sized enterprises. You will leverage your strong data analysis skills with SQL and Python to optimize credit risk strategies and work closely with the Director of Credit Risk Management.
This hybrid role offers opportunities for working on cross-functional projects, contributing to a data-driven culture, and benefiting from flexible working conditions.
SME Credit Risk Analyst — Data-Driven, Hybrid London employer: Pliant
Contact Detail:
Pliant Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land SME Credit Risk Analyst — Data-Driven, Hybrid London
✨Tip Number 1
Network like a pro! Reach out to folks in the fintech space, especially those working with credit risk. A friendly chat can open doors and give you insights that might just land you that interview.
✨Tip Number 2
Show off your skills! If you've got experience with SQL and Python, consider creating a small project or analysis to showcase your abilities. Share it on LinkedIn or during interviews to demonstrate your data-driven mindset.
✨Tip Number 3
Prepare for the interview by brushing up on common credit risk scenarios. Think about how you'd approach building automated credit strategies and be ready to discuss your thought process with the hiring team.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive and engaged with our company.
We think you need these skills to ace SME Credit Risk Analyst — Data-Driven, Hybrid London
Some tips for your application 🫡
Show Off Your Data Skills: Make sure to highlight your experience with SQL and Python in your application. We want to see how you've used these skills to tackle real-world problems, especially in credit risk analysis.
Tailor Your Application: Don’t just send a generic CV and cover letter. We love it when candidates customise their applications to reflect the job description. Mention specific projects or experiences that relate to building automated credit strategies for SMEs.
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon and get straight to the point about your qualifications and what you can bring to our team.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our data-driven culture.
How to prepare for a job interview at Pliant
✨Know Your Data Tools
Make sure you brush up on your SQL and Python skills before the interview. Be ready to discuss how you've used these tools in past projects, especially in relation to credit risk analysis. This will show that you can hit the ground running!
✨Understand Credit Risk Strategies
Familiarise yourself with automated credit strategies, particularly for SMEs. Research current trends in fintech and be prepared to share your thoughts on how data-driven approaches can enhance credit risk management.
✨Prepare for Cross-Functional Questions
Since this role involves working on cross-functional projects, think about examples from your past experiences where you've collaborated with different teams. Highlight your communication skills and how you can contribute to a data-driven culture.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions! Inquire about the company's approach to credit risk management or how they leverage data analytics. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
