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- Tasks: Join our team to build Credit Risk Models and Scorecards using Machine Learning.
- Company: We're a dynamic SME lending FinTech based in London, driving innovation in finance.
- Benefits: Work 3 days a week in the office with a vibrant team culture.
- Why this job: Make an impact in the FinTech space while developing your data science skills.
- Qualifications: Experience in Credit Risk Models, Machine Learning, and proficiency in Python or R required.
- Other info: UK lending experience is a must; no sponsorship available.
The predicted salary is between 48000 - 72000 £ per year.
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).
Senior Credit Modeller / Data Scientist employer: InterQuest Group
Contact Detail:
InterQuest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Credit Modeller / Data Scientist
✨Tip Number 1
Make sure to highlight your experience with traditional Credit Risk Models and Scorecards in your conversations. Be ready to discuss specific projects where you successfully implemented these models, as this will demonstrate your expertise.
✨Tip Number 2
Familiarize yourself with the latest trends in Machine Learning and how they apply to credit modeling. Being able to discuss recent advancements or case studies can set you apart from other candidates.
✨Tip Number 3
Since the role requires UK lending experience, prepare to share insights about the UK lending landscape. This could include regulatory considerations or market trends that impact SME lending.
✨Tip Number 4
Network with professionals in the FinTech space, especially those focused on lending. Engaging in relevant discussions can provide you with valuable insights and potentially lead to referrals for the position.
We think you need these skills to ace Senior Credit Modeller / Data Scientist
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, as this could be valuable to the hiring manager.
Craft a Strong Cover Letter: Write a compelling cover letter that explains why you are a great fit for the role. Discuss your passion for data science and credit modelling, and how your background aligns with the needs of the FinTech client.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any grammatical errors or typos, and ensure that all information is clear and concise.
How to prepare for a job interview at InterQuest Group
✨Showcase Your Technical Skills
Be prepared to discuss your experience with building Credit Risk Models and Scorecards. Highlight specific projects where you utilized Python or R, and be ready to explain your approach and the outcomes.
✨Demonstrate Your Understanding of SME Lending
If you have SME lending experience, make sure to emphasize it during the interview. If your background is in consumer lending, draw parallels and explain how your skills can transfer to the SME sector.
✨Prepare for Machine Learning Questions
Expect questions related to machine learning techniques and their application in credit modeling. Brush up on relevant algorithms and be ready to discuss how you've implemented them in past projects.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to credit modeling and data science. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.
