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- Tasks: Join a dynamic team to develop pricing models and support trading with quantitative analysis.
- Company: A top investment bank known for its innovative approach in finance.
- Benefits: Enjoy competitive pay, potential remote work options, and a vibrant workplace culture.
- Why this job: Be at the forefront of finance, working directly with traders and making impactful decisions.
- Qualifications: MSc or PhD in a quantitative field; strong programming skills in C++ or Python required.
- Other info: Ideal for those passionate about finance and eager to innovate in a fast-paced environment.
The predicted salary is between 48000 - 72000 £ per year.
A leading investment bank is looking for a talented Front Office Quantitative Analyst to join its high-performing Equity & Hybrid Products team in London. This is a front-office role, sitting directly with the trading desk, focused on the pricing, modeling, and risk management of complex equity and cross-asset derivatives.
Key Responsibilities:
- Develop and enhance pricing and risk models for equity and hybrid structures (e.g., equity-rate, equity-FX).
- Implement models in C++, Python, or in-house quantitative libraries.
- Calibrate models to real-time market data and support trading with quantitative analysis.
- Work closely with traders, structurers, and risk teams to deliver real-time tools and analytics.
- Ensure model governance standards are met, including documentation and validation support.
- Innovate on numerical methods and contribute to pricing methodology enhancements.
- Run scenario analysis and stress testing for structured equity and hybrid products.
Requirements:
- MSc or PhD in Mathematics, Physics, Financial Engineering, Computer Science, or a related quantitative discipline.
- Proven experience with pricing equity derivatives, including vanillas and exotics.
- Strong programming skills in C++ and/or Python.
- Solid grasp of stochastic calculus, numerical techniques, and financial modeling.
- Familiarity with hybrid product structures is highly advantageous.
- Previous front-office quant or risk/valuation experience preferred.
- Excellent communication skills with the ability to explain models to non-technical audiences.
Equity Quantitative Analyst employer: Alexander Chapman
Contact Detail:
Alexander Chapman Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Equity Quantitative Analyst
✨Tip Number 1
Network with professionals in the finance and quantitative analysis sectors. Attend industry conferences, webinars, or local meetups to connect with people who work in investment banks or similar roles. This can help you gain insights into the company culture and potentially get referrals.
✨Tip Number 2
Brush up on your programming skills, especially in C++ and Python. Consider working on personal projects or contributing to open-source projects that involve financial modelling or quantitative analysis. This will not only enhance your skills but also provide you with practical examples to discuss during interviews.
✨Tip Number 3
Stay updated on the latest trends and developments in equity derivatives and hybrid products. Read relevant financial journals, blogs, and research papers to deepen your understanding of pricing models and risk management techniques. This knowledge will be invaluable during discussions with potential employers.
✨Tip Number 4
Prepare to demonstrate your ability to communicate complex quantitative concepts clearly. Practice explaining your past projects or models to a non-technical audience, as this skill is crucial for collaborating with traders and other teams. Being able to bridge the gap between technical and non-technical stakeholders can set you apart.
We think you need these skills to ace Equity Quantitative Analyst
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in quantitative analysis, particularly with equity derivatives. Emphasise your programming skills in C++ and Python, as well as any experience with financial modelling or risk management.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss specific projects or experiences that demonstrate your ability to develop and enhance pricing models, and how you can contribute to the trading desk.
Showcase Technical Skills: Include a section in your application that showcases your technical skills. Mention any relevant coursework, projects, or work experience that involved stochastic calculus, numerical techniques, or model governance standards.
Prepare for Interviews: If selected for an interview, be ready to discuss your previous work in detail. Prepare to explain complex models in simple terms, as communication skills are crucial for this role. Practice common quantitative interview questions related to pricing and risk management.
How to prepare for a job interview at Alexander Chapman
✨Showcase Your Technical Skills
Be prepared to discuss your programming experience in C++ and Python. You might be asked to solve a coding problem or explain how you've implemented models in the past, so brush up on your technical knowledge and be ready to demonstrate your skills.
✨Understand the Role of a Quantitative Analyst
Make sure you have a solid understanding of what a Front Office Quantitative Analyst does, especially in relation to pricing and risk management of equity derivatives. Familiarise yourself with key concepts like stochastic calculus and numerical techniques, as these may come up during the interview.
✨Prepare for Scenario Analysis Questions
Since the role involves running scenario analysis and stress testing, be ready to discuss how you would approach these tasks. Think about real-world examples where you've applied these techniques and be prepared to explain your thought process clearly.
✨Communicate Effectively
Excellent communication skills are crucial for this role. Practice explaining complex quantitative concepts in simple terms, as you may need to convey your ideas to non-technical stakeholders. This will demonstrate your ability to bridge the gap between technical and non-technical teams.
