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- Tasks: Join a dynamic team to build Credit Risk Models and Scorecards using Machine Learning.
- Company: Be part of an innovative SME lending FinTech based in London.
- Benefits: Enjoy a hybrid work model with 3 days in the office each week.
- Why this job: Make an impact in the FinTech space while advancing your data science skills.
- Qualifications: Experience in Credit Risk Modelling, Machine Learning, and proficiency in Python or R required.
- Other info: UK lending experience is essential; no sponsorship available.
The predicted salary is between 48000 - 84000 £ per year.
🔥 New Credit Modelling & Data Science role – FinTech – London 🔥
I am looking for a new Senior Credit Modeller/Data Science to join an SME lending FinTech client of mine based in London.
If you have experience with building traditional Credit Risk Models & Scorecards, as well as experience within Machine Learning and Python or R – get in touch for more info!
SME lending experience would be fantastic, but if you have consumer lending expeirence – the hiring manager is happy to consider this.
(3 days per week in the London office, no sponsorship available and candidates must have UK lending experience).
InterQuest Group | Senior Credit Risk Modeller / Data Scientist - FinTech employer: InterQuest Group
Contact Detail:
InterQuest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land InterQuest Group | Senior Credit Risk Modeller / Data Scientist - FinTech
✨Tip Number 1
Make sure to highlight your experience with traditional credit risk models and scorecards during the interview. Be prepared to discuss specific projects where you successfully implemented these models.
✨Tip Number 2
Familiarize yourself with the latest trends in machine learning as they apply to credit risk modeling. Being able to discuss how you've integrated machine learning techniques into your work will set you apart.
✨Tip Number 3
Since the role is within a FinTech environment, be ready to demonstrate your understanding of the SME lending landscape. Research the challenges and opportunities in this sector to show your enthusiasm and knowledge.
✨Tip Number 4
Network with professionals in the FinTech space, especially those focused on credit risk. Engaging with industry events or online forums can provide valuable insights and connections that may help you during the application process.
We think you need these skills to ace InterQuest Group | Senior Credit Risk Modeller / Data Scientist - FinTech
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in building traditional Credit Risk Models and Scorecards. If you have worked with Machine Learning, Python, or R, showcase specific projects or achievements that demonstrate your skills.
Tailor Your CV: Customize your CV to align with the job description. Focus on your SME lending experience, but also mention any consumer lending experience you have. Use keywords from the job listing to make your application stand out.
Craft a Strong Cover Letter: Write a compelling cover letter that explains why you are a great fit for this role. Discuss your passion for FinTech and how your background in credit risk modelling can contribute to the company's success.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any spelling or grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail.
How to prepare for a job interview at InterQuest Group
✨Showcase Your Technical Skills
Make sure to highlight your experience with Python or R during the interview. Be prepared to discuss specific projects where you built credit risk models or scorecards, and how you applied machine learning techniques.
✨Understand the FinTech Landscape
Familiarize yourself with the current trends in the FinTech industry, especially related to SME lending. This will help you demonstrate your knowledge and passion for the field, which can set you apart from other candidates.
✨Prepare for Behavioral Questions
Expect questions about your previous experiences and how you've handled challenges in credit risk modeling. Use the STAR method (Situation, Task, Action, Result) to structure your answers effectively.
✨Ask Insightful Questions
Prepare thoughtful questions to ask the interviewer about the company's approach to credit risk modeling and their data science initiatives. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
