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- Tasks: Develop and implement quantitative 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 a hybrid work model and access to corporate perks.
- Why this job: Make a real impact by supporting Treasury Finance and enhancing your analytical skills.
- Qualifications: Solid Python programming skills and experience with large datasets are essential.
- Other info: This role offers opportunities for ongoing model validation and performance monitoring.
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.
- 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.
- 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.
- Solid Python programming skills and experience handling large datasets.
Implementation Analyst (Hybrid) employer: Radley James
Contact Detail:
Radley James Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Implementation Analyst (Hybrid)
✨Tip Number 1
Familiarise yourself with the latest trends in asset-liability management 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 writing production-ready code. Consider working on personal projects or contributing to open-source projects that involve statistical modelling to showcase your abilities.
✨Tip Number 3
Network with professionals in the financial sector, particularly those who work in quantitative analytics. Attend industry events or join relevant online forums to gain insights and potentially get referrals.
✨Tip Number 4
Prepare to discuss your experience with model validation and performance monitoring. Be ready to provide examples of how you've ensured compliance with governance frameworks in your previous roles.
We think you need these skills to ace Implementation Analyst (Hybrid)
Some tips for your application 🫡
Understand the Role: Familiarise yourself with the responsibilities of an Implementation Analyst in a financial institution. Highlight your experience with statistical models, asset-liability forecasting, and interest rate risk management in your application.
Showcase Your Technical Skills: Emphasise your Python programming skills and experience with large datasets. Provide specific examples of projects where you developed production-ready code or utilised econometric techniques like time series analysis and regression modelling.
Tailor Your CV: Customise your CV to reflect the key skills and experiences mentioned in the job description. Use relevant keywords that align with the role, such as 'model validation', 'documentation', and 'performance monitoring'.
Craft a Compelling Cover Letter: Write a cover letter that connects your background to the specific needs of the Treasury Quantitative Analytics team. Discuss your understanding of model governance and risk frameworks, and express your enthusiasm for contributing to their objectives.
How to prepare for a job interview at Radley James
✨Showcase Your Python Skills
Since the role requires solid Python programming skills, be prepared to discuss your experience with Python in detail. Bring examples of projects where you've written production-ready code, especially those involving statistical models or large datasets.
✨Understand Econometric Techniques
Brush up on advanced econometric and statistical techniques like time series analysis and regression modelling. Be ready to explain how you've applied these methods in past roles, as this will demonstrate your capability to develop and implement quantitative models.
✨Familiarise Yourself with Model Governance
The position involves ensuring compliance with internal governance and model risk frameworks. Research the relevant regulations and be prepared to discuss how you have previously ensured compliance in your work, highlighting any specific frameworks you are familiar with.
✨Prepare for Technical Questions
Expect technical questions related to asset-liability forecasting and interest rate risk management. Review key concepts and be ready to solve problems on the spot, as this will showcase your analytical thinking and problem-solving abilities.
