Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Revamp credit risk models using Python and collaborate with quants and risk teams.
- Company: Leading global investment bank in the heart of London.
- Benefits: Competitive day rate, potential for permanent role, and exposure to diverse asset classes.
- Other info: Dynamic team environment with opportunities for career growth and learning.
- Why this job: Join a high-priority programme and make a real impact in modernising financial models.
- Qualifications: Strong Python skills and experience in credit risk or similar asset classes.
A leading global investment bank in the City of London is looking to hire a Quant Developer - Credit on a contract basis. This is an initial 6 month contract with the option to extend, paying a day rate in the region of £800 to £925 per day. This role sits within a Quantitative Analytics function, initially aligned to a credit focussed programme supporting the development and improvement of risk models. The focus is on modernising existing models and improving how they are built, structured and maintained, rather than pure Front Office pricing. The business is going through a broader modernisation programme and is looking for someone to play a key role in that transition. There is also scope to gain exposure to other asset classes and areas of the business over time, as well as the opportunity to move into a permanent role if of interest.
Key responsibilities
- Redevelop and translate existing quantitative models into Python
- Improve model structure, performance and maintainability
- Work closely with quants and risk teams to understand model logic and outputs
- Support testing, validation and deployment into production
- Contribute to development best practices including version control and testing
Experience required
- Strong Python development experience within a quant or risk environment
- Experience working with credit risk, Fixed Income or similar asset classes
- Good understanding of econometrics or time series modelling
- Experience working within financial services, ideally banking
- Comfortable working across the full development life cycle
Beneficial
- Experience working with or translating R code
- Exposure to C++ or working within mixed technology environments
- Front Office or desk facing exposure working with quants or traders
- Knowledge of derivatives, XVA or broader risk modelling
- Experience working with modern data platforms or cloud environments
This is a strong opportunity to join a well-established team working on a high priority programme within a global bank, with scope to broaden your exposure beyond credit over time. If you are a Quant Developer with the above experience, please respond with an up-to-date CV.
Quant Developer - Credit employer: McCabe & Barton
Contact Detail:
McCabe & Barton Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quant Developer - Credit
✨Tip Number 1
Network like a pro! Reach out to your connections in the finance and tech sectors. Attend meetups or webinars related to quantitative development and credit risk. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your Python projects, especially those related to quantitative models or risk analysis. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with econometrics, time series modelling, and any relevant projects. Practice coding challenges that focus on Python and quantitative analysis.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be perfect for you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace Quant Developer - Credit
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python development experience and any relevant work in credit risk or similar areas. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about this Quant Developer role and how your background makes you a perfect fit. We love seeing genuine enthusiasm for the position.
Showcase Your Technical Skills: Since this role involves modernising models and working with various technologies, make sure to mention any experience with econometrics, time series modelling, or even R and C++. We want to know what tools you’ve got in your toolkit!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it’s super easy!
How to prepare for a job interview at McCabe & Barton
✨Know Your Python Inside Out
Make sure you brush up on your Python skills, especially in the context of quantitative modelling. Be ready to discuss specific projects where you've redeveloped models or improved performance. Practising coding challenges can also help you demonstrate your proficiency during the interview.
✨Understand Credit Risk Fundamentals
Since this role focuses on credit risk, it's crucial to have a solid grasp of the concepts involved. Familiarise yourself with key terms and methodologies related to credit risk and be prepared to discuss how you've applied these in previous roles. This will show that you can hit the ground running.
✨Showcase Your Collaborative Spirit
This position requires working closely with quants and risk teams, so highlight your teamwork skills. Prepare examples of how you've successfully collaborated on projects, particularly in a financial services environment. This will demonstrate your ability to integrate into their existing team dynamics.
✨Be Ready for Technical Questions
Expect technical questions that assess your understanding of econometrics and time series modelling. Brush up on relevant theories and be prepared to explain how you've used them in practice. This will not only showcase your expertise but also your problem-solving approach.