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For Companies At a Glance
- Tasks: Develop credit risk models and scorecards using advanced statistical methods.
- Company: Join a dynamic FinTech focused on consumer lending innovation.
- Benefits: Enjoy a flexible day rate contract with potential for remote work.
- Why this job: Be part of impactful projects that shape business strategy and customer insights.
- Qualifications: Must have 10+ years in credit risk modelling with strong SQL and Python skills.
- Other info: No sponsorship available; ideal for experienced professionals seeking contract work.
Job Description
I am currently looking for a Credit Risk Modeller to join a consumer lending FinTech on a day rate contract basis.
The role will include developing a PD Model and other Credit Models/Scorecards using logistic regression and gradient boosting methods. There will also be work/projects within business strategy, churn likelihood, potential for leaving and complexion models. The ideal candidate should have strong SQL & Python and the candidate needs to have experience developing models for consumer lenders (personal loans or credit cards).
Must have 10+ years within Credit Risk Modelling.
(No sponsorship available for the role)
Credit Risk Modeller - Day Rate Contract employer: InterQuest Group
Contact Detail:
InterQuest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Credit Risk Modeller - Day Rate Contract
✨Tip Number 1
Make sure to highlight your experience with developing PD Models and other credit models in your conversations. Be ready to discuss specific projects where you've successfully implemented logistic regression and gradient boosting methods.
✨Tip Number 2
Brush up on your SQL and Python skills before any interviews. You might be asked to solve a problem or demonstrate your coding abilities, so having practical examples ready can really set you apart.
✨Tip Number 3
Familiarise yourself with the latest trends in consumer lending and credit risk modelling. Being able to discuss current challenges and innovations in the industry will show that you're not just experienced, but also engaged and forward-thinking.
✨Tip Number 4
Network with professionals in the FinTech space, especially those focused on credit risk. Attend relevant meetups or webinars to make connections that could lead to referrals or insider information about the role.
We think you need these skills to ace Credit Risk Modeller - Day Rate Contract
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Credit Risk Modelling, particularly with consumer lenders. Emphasise your skills in SQL and Python, and include specific examples of models you've developed using logistic regression and gradient boosting methods.
Craft a Strong Cover Letter: In your cover letter, explain why you're a great fit for the role. Mention your 10+ years of experience in Credit Risk Modelling and how it aligns with the company's needs. Be sure to touch on your familiarity with business strategy and churn likelihood projects.
Showcase Relevant Projects: If you have worked on specific projects related to PD Models or other credit scorecards, summarise these in your application. Highlight the outcomes and any impact they had on the business, as this will demonstrate your practical experience.
Proofread Your Application: Before submitting, carefully proofread your application for any errors or typos. A polished application reflects your attention to detail, which is crucial in a role that involves developing complex models.
How to prepare for a job interview at InterQuest Group
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in SQL and Python during the interview. Be prepared to discuss specific projects where you've used these skills, especially in developing credit risk models.
✨Demonstrate Your Experience
With over 10 years in credit risk modelling, you should be ready to share detailed examples of your past work. Discuss the models you've developed for consumer lenders, focusing on personal loans or credit cards.
✨Understand the Business Context
Familiarise yourself with the FinTech landscape and the specific challenges faced by consumer lenders. This will help you articulate how your modelling experience can contribute to their business strategy and churn likelihood projects.
✨Prepare for Technical Questions
Expect to answer technical questions related to logistic regression and gradient boosting methods. Brush up on these topics and be ready to explain your approach to model development and validation.
