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- Tasks: Develop credit risk models and analytics to optimise lending decisions.
- Company: Join a rapidly expanding financial services company making waves in consumer lending.
- Benefits: Enjoy opportunities for growth, remote work options, and a dynamic work environment.
- Why this job: Be part of a team that shapes the future of lending with innovative data solutions.
- Qualifications: Experience in financial services, proficiency in Python, R, SQL, and strong analytical skills required.
- Other info: Ideal for those who thrive in fast-paced environments and love working with data.
The predicted salary is between 48000 - 72000 £ per year.
This rapidly expanding financial services company is seeking a Senior Credit Risk Analyst to join their Consumer Lending function. Working with the Commercial Director, you will develop credit risk analytics and scorecard modelling solutions to enhance Credit Scoring and Lending decision-making to optimise and grow their loan portfolio.
Key Responsibilities:
- Developing and implementing advanced statistical and scorecard models to predict credit risk, optimise credit scoring, and enhance decision-making/underwriting processes.
- Develop and maintain predictive models to assess credit risk and forecast customer behaviour.
- Analyse large datasets to identify trends, patterns, and insights that inform business decisions.
- Perform data cleaning to ensure high-quality data for analysis.
- Conduct A/B testing and other experiments to evaluate the impact of credit strategies and policies.
- Develop credit risk models, such as probability of default (PD), using various modelling techniques.
- Work independently and present findings and recommendations to stakeholders in a clear and concise manner.
Key Skills / Experience:
- Experience in the Financial Services Industry (Essential)
- Experience working with large data sets (Essential)
- Proficiency in Python, R, SQL or other programming languages (Essential)
- Proficiency in Excel (Essential)
- Strong presentation skills, including the ability to translate complex data into understandable insights (Essential)
- A great attention to detail and a process-oriented approach to review, suggest and implement improvements where appropriate (Essential)
- Able to work in a fast-paced, changing environment (Essential)
- Degree in a relevant subject (Data Science, Statistics, Computer Science, Economics or similar degree) (Preferable)
- Experience using Salesforce and data visualisation tools (Preferable)
What’s on Offer:
Opportunity to develop and enhance credit risk modelling and analytics strategy. Opportunity to join a rapidly expanding financial services company.
Senior Credit Risk Analyst - Consumer Lending / Loans employer: Michael Page (UK)
Contact Detail:
Michael Page (UK) Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Credit Risk Analyst - Consumer Lending / Loans
✨Tip Number 1
Familiarise yourself with the latest trends in credit risk modelling and analytics. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews, showcasing your knowledge and enthusiasm for the field.
✨Tip Number 2
Network with professionals in the financial services industry, especially those who work in credit risk. Attend relevant webinars or local meetups to build connections and gain insights that could give you an edge when applying for the position.
✨Tip Number 3
Brush up on your programming skills, particularly in Python, R, and SQL. Consider working on personal projects or contributing to open-source projects that involve data analysis or credit risk modelling to demonstrate your proficiency.
✨Tip Number 4
Prepare to discuss your experience with large datasets and how you've used them to derive insights. Be ready to share specific examples of how your analytical skills have positively impacted decision-making in previous roles.
We think you need these skills to ace Senior Credit Risk Analyst - Consumer Lending / Loans
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in the financial services industry, particularly with credit risk analytics and large datasets. Use specific examples to demonstrate your proficiency in Python, R, SQL, and Excel.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your skills align with the key responsibilities, such as developing predictive models and conducting A/B testing, and provide examples of past successes.
Showcase Your Analytical Skills: When detailing your experience, focus on your ability to analyse large datasets and present findings. Highlight any projects where you translated complex data into actionable insights, as this is crucial for the role.
Proofread and Edit: Before submitting your application, thoroughly proofread your documents. Ensure there are no grammatical errors or typos, as attention to detail is essential for this position. Consider asking a friend or colleague to review your application as well.
How to prepare for a job interview at Michael Page (UK)
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in Python, R, SQL, and Excel during the interview. Be prepared to discuss specific projects where you've used these tools to analyse large datasets or develop predictive models.
✨Prepare for Data Analysis Questions
Expect questions that assess your ability to analyse data and identify trends. Brush up on your statistical knowledge and be ready to explain how you would approach a credit risk modelling problem using real-world examples.
✨Demonstrate Strong Presentation Skills
Since strong presentation skills are essential for this role, practice explaining complex data insights in a clear and concise manner. You might be asked to present findings from a previous project, so have a few examples ready.
✨Emphasise Attention to Detail
Given the importance of data quality in credit risk analysis, be prepared to discuss how you ensure accuracy in your work. Share examples of how you've implemented processes to improve data quality and decision-making.
