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- Tasks: Join a dynamic team to build innovative Credit Risk Models and Scorecards.
- Company: Work with a cutting-edge SME lending FinTech based in London.
- Benefits: Enjoy a hybrid work model with 3 days in the office and flexible hours.
- Why this job: Be part of a fast-paced environment where your data science skills can shine and make an impact.
- Qualifications: Experience in Credit Risk Modeling, Machine Learning, and proficiency in Python or R required.
- Other info: Candidates must have UK lending experience; no sponsorship available.
The predicted salary is between 43200 - 72000 £ per year.
New Credit Modelling & Data Science role – FinTech – London
I am looking for a new Senior Credit Modeller/Data Scientist 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 experience – 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 is acting as an employment agency for this vacancy. InterQuest Group is an equal opportunities employer and we welcome applications from all suitably qualified persons regardless of age, disability, gender, religion/belief, race, marriage, civil partnership, pregnancy, maternity, sex or sexual orientation. Please make us aware if you require any reasonable adjustments throughout the recruitment process.
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Senior Credit Modeller - Data Scientist employer: InterQuest Solutions
Contact Detail:
InterQuest Solutions 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.
✨Tip Number 2
Familiarize yourself with the latest trends in Machine Learning as they relate to credit modelling. Being able to discuss recent advancements or case studies can set you apart during interviews.
✨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 credit modelling. Engaging with industry events or online forums can provide valuable connections and insights that may help you during the application process.
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 SME lending or consumer lending, be sure to mention this prominently in your application.
Showcase Technical Skills: Clearly outline your proficiency in Machine Learning, Python, and R. Provide specific examples of projects or tasks where you utilized these skills to demonstrate your expertise.
Tailor Your CV: Customize your CV to align with the job description. Use keywords from the posting, such as 'Credit Modeller', 'Data Scientist', and 'FinTech', to ensure your application stands out to recruiters.
Craft a Compelling Cover Letter: Write a cover letter that not only summarizes your qualifications but also expresses your enthusiasm for the role and the company. Mention why you are particularly interested in working in the FinTech sector and how you can contribute to their success.
How to prepare for a job interview at InterQuest Solutions
✨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 experience in SME lending, make sure to share relevant examples. 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 algorithms you've used and be ready to discuss their advantages and limitations.
✨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 it's the right fit for you.
