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- Tasks: Develop and implement models for asset-liability forecasting and interest rate risk management.
- Company: Join a leading global financial institution with a strong reputation in the finance sector.
- Benefits: Enjoy competitive salary, potential remote work options, and opportunities for professional growth.
- Why this job: Make a real impact in Treasury Finance while working with advanced statistical techniques and a dynamic team.
- Qualifications: Strong background in ALM or Treasury modelling, Python skills, and excellent communication abilities required.
- Other info: This role is based in London and offers a chance to work on high-impact projects.
The predicted salary is between 48000 - 72000 £ per year.
We’re working with a leading global financial institution seeking an experienced ALM Quantitative Analyst (AVP level) to join their Treasury Quantitative Analytics team in London. This is a high-impact role supporting Treasury Finance by developing statistical models to forecast behavioural asset and liability balances—key to managing interest rate risk.
Key Responsibilities:
- Develop and implement quantitative models for asset-liability forecasting and interest rate risk management.
- Utilise advanced econometric and statistical techniques such as time series analysis and regression modelling.
- Translate complex technical concepts for both technical and non-technical stakeholders.
- Write robust, production-ready Python code and partner with technology teams to operationalise models.
- Ensure models are compliant with internal governance and model risk frameworks.
- Provide ongoing model validation, documentation, and performance monitoring.
Ideal Candidate Will Have:
- Strong background in ALM or Treasury modelling, particularly behavioural balance forecasting.
- Deep knowledge of statistical/econometric methods.
- Solid Python programming skills and experience handling large datasets.
- Clear and confident communicator, able to bridge the technical and business gap.
Quantitative Analyst employer: Radley James
Contact Detail:
Radley James Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Analyst
✨Tip Number 1
Familiarise yourself with the latest trends in asset-liability management (ALM) and interest rate risk. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Brush up on your Python programming skills, especially in relation to statistical modelling. Consider working on personal projects or contributing to open-source projects that involve large datasets to showcase your coding abilities.
✨Tip Number 3
Prepare to explain complex technical concepts in simple terms. Practising how to communicate your ideas clearly can set you apart, especially since the role requires bridging the gap between technical and non-technical stakeholders.
✨Tip Number 4
Network with professionals in the financial sector, particularly those in Treasury or ALM roles. Attend industry events or join relevant online forums to gain insights and potentially get referrals for the position.
We think you need these skills to ace Quantitative Analyst
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities of a Quantitative Analyst in Treasury. Familiarise yourself with asset-liability management and interest rate risk concepts to tailor your application effectively.
Highlight Relevant Experience: In your CV and cover letter, emphasise your experience with statistical modelling, particularly in ALM or Treasury contexts. Provide specific examples of projects where you've developed models or used econometric techniques.
Showcase Technical Skills: Make sure to detail your Python programming skills and any experience with large datasets. Mention specific libraries or frameworks you’ve used, as this will demonstrate your technical proficiency to potential employers.
Communicate Clearly: Since the role requires translating complex concepts for various stakeholders, ensure your application reflects your communication skills. Use clear, concise language and avoid jargon where possible to show you can bridge the gap between technical and non-technical audiences.
How to prepare for a job interview at Radley James
✨Showcase Your Technical Skills
Be prepared to discuss your experience with statistical models and econometric techniques. Highlight specific projects where you've developed models for asset-liability forecasting or interest rate risk management, and be ready to explain the methodologies you used.
✨Demonstrate Python Proficiency
Since solid Python programming skills are crucial for this role, come equipped with examples of your coding work. You might even want to prepare a small coding exercise or be ready to discuss how you've operationalised models in the past.
✨Communicate Clearly
As the role requires translating complex concepts for various stakeholders, practice explaining your work in simple terms. Think about how you can convey technical information to non-technical audiences without losing the essence of your analysis.
✨Understand Governance and Compliance
Familiarise yourself with model risk frameworks and internal governance processes. Be ready to discuss how you've ensured compliance in your previous roles, as this will demonstrate your understanding of the importance of these aspects in quantitative analysis.
