At a Glance
- Tasks: Develop and maintain a cutting-edge asset simulation platform using Python and C++.
- Company: Global asset manager based in London, known for innovation and excellence.
- Benefits: Competitive salary, professional development, and opportunities for career advancement.
- Other info: Collaborative environment with a focus on automation and scalability in research processes.
- Why this job: Join a dynamic team and make a real impact on financial modelling and investment strategies.
- Qualifications: Master’s degree in relevant fields and 3-5 years of quantitative experience required.
The predicted salary is between 60000 - 80000 € per year.
Responsibilities
- Develop, maintain and calibrate a proprietary asset simulation platform
- Model capital market assumptions and produce asset class simulations
- Design and implement macro-financial models in Python and/or C++
- Adapt internal models to specific optimisation and simulation requirements
- Build ad‑hoc analytical tools in Python and Excel to deliver customised solutions
- Support Strategic Asset Allocation, ALM and lifecycle investing (including decumulation)
- Contribute to forecasts and portfolio construction best practice across geographies and asset classes
- Provide technical support to sales/clients and present methods and results clearly
- Write clean, tested code; use Git and deploy into production environments
- Drive automation and scalability across quantitative research processes
Requirements
- Master’s degree in Mathematics, Statistics, Computer Science, Economics or Financial Engineering
- 3–5 years’ experience as a quantitative analyst/programmer in an asset manager or investment bank
- Strong foundation in probability theory, stochastic calculus and statistical inference
- Experience modelling liquid and illiquid asset classes, asset allocation and portfolio optimisation
- Hands‑on exposure to bond pricing, stochastic volatility modelling and Monte Carlo simulations
- Proficient in time‑series analysis, econometrics and factor‑based modelling
- Advanced Python (numpy, pandas); experience deploying code to production
- C++ a strong advantage; SQL proficiency; MS Office with VBA a plus
Quantitative Analyst/Quantitative Programmer, Global Asset Manager, London - eFinancialCareers employer: Jobs via eFinancialCareers
As a leading global asset manager based in London, we pride ourselves on fostering a dynamic and inclusive work culture that encourages innovation and collaboration. Our employees benefit from comprehensive professional development opportunities, competitive compensation packages, and the chance to work with cutting-edge technology in a vibrant financial hub. Join us to make a meaningful impact in the world of finance while enjoying a supportive environment that values your contributions and growth.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Analyst/Quantitative Programmer, Global Asset Manager, London - eFinancialCareers
✨Tip Number 1
Network like a pro! Reach out to professionals in the asset management field on LinkedIn or at industry events. We can’t stress enough how personal connections can open doors that applications alone can’t.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in Python and C++. We want to see your coding chops and how you’ve tackled real-world problems, so make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with asset allocation and portfolio optimisation. We recommend practising common quantitative analysis questions to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Quantitative Analyst/Quantitative Programmer, Global Asset Manager, London - eFinancialCareers
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your expertise in Python, C++, and any relevant quantitative analysis you've done. We want to see how you can contribute to our asset simulation platform!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about quantitative analysis and how your background aligns with our needs. Don’t forget to mention your experience with capital market assumptions and portfolio optimisation.
Showcase Your Projects:If you've worked on any relevant projects, whether in a professional setting or as personal endeavours, make sure to include them. We love seeing practical applications of your skills, especially in macro-financial models and automation!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Jobs via eFinancialCareers
✨Know Your Models Inside Out
Make sure you’re well-versed in the macro-financial models and asset class simulations mentioned in the job description. Be ready to discuss your experience with Python and C++, and how you've applied these skills in real-world scenarios. Practising explaining complex concepts in simple terms can really help you shine.
✨Showcase Your Analytical Skills
Prepare to demonstrate your analytical prowess by discussing specific projects where you’ve built ad-hoc analytical tools or contributed to portfolio construction. Bring examples of how you’ve used time-series analysis or econometrics to solve problems, as this will show your practical application of theory.
✨Brush Up on Technical Skills
Since coding is a big part of this role, be prepared for technical questions or even a coding test. Review your knowledge of Python libraries like numpy and pandas, and ensure you can write clean, tested code. Familiarise yourself with Git workflows, as they may ask about your experience deploying code into production environments.
✨Communicate Clearly and Confidently
You’ll need to present methods and results clearly, so practice articulating your thought process and findings. Use mock interviews to refine your communication style, ensuring you can explain complex quantitative concepts in an accessible way. Remember, confidence is key!
