Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join the QA Equity & Hybrid Products team to develop and implement quantitative models.
- Company: Be part of a global analytics and digital solutions firm making waves in finance.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for professional growth.
- Why this job: Work on cutting-edge financial models and collaborate with top industry professionals.
- Qualifications: Masters or PhD in Mathematics/Computer Science; strong C++ and Python skills required.
- Other info: Ideal for mathematically minded individuals passionate about financial engineering.
The predicted salary is between 43200 - 72000 £ per year.
Our client is a global analytics and digital solutions firm. The candidate will be a member of The QA Equity & Hybrid Products team, which is part of the Global Quantitative Analytics group (QA) and is responsible for research, development and implementation of quantitative models for the equity derivatives business. It covers equity flow products, equity structured & hybrid products, quantitative index and strategies business.
Responsibilities include:
- Documentation, testing and improvements of an internal risk model, which is owned by The QA Equity & Hybrid Products team and feeds the Credit Valuation Adjustment (CVA) for products sensitive to future implied volatility dynamics.
- Liaising with Front Office, Technology and Model Validation teams to deploy the risk model to production.
Qualifications:
- Masters or PhD in Mathematics/Computer Science or related field.
- Mathematically minded (knowledge of financial mathematics, ability to program numerical algorithms in C++).
- Experience in Front Office & Derivatives modelling.
- Theoretical knowledge of financial engineering/structuring and financial product development.
- Good Python programming skills.
- Strong C++ programming skills (ideally worked in a C++ shared library).
- Integrity, desire to have correct and robust mathematical models and implementation.
- Good written and verbal communication in English.
PhD Analysts employer: Morgan McKinley
Contact Detail:
Morgan McKinley Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land PhD Analysts
✨Tip Number 1
Familiarise yourself with the latest trends in quantitative finance and equity derivatives. 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 C++ programming skills, especially focusing on numerical algorithms and shared libraries. Consider working on personal projects or contributing to open-source projects to showcase your coding abilities.
✨Tip Number 3
Network with professionals in the field of quantitative analytics and financial engineering. Attend industry conferences or webinars to meet potential colleagues and learn more about the company culture at firms like ours.
✨Tip Number 4
Prepare to discuss your experience with risk models and how you've implemented them in past roles. Be ready to provide examples of how you've collaborated with different teams, such as Front Office and Technology, to enhance model performance.
We think you need these skills to ace PhD Analysts
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in quantitative analytics, programming skills in C++, and any work related to equity derivatives. Use specific examples that demonstrate your expertise in financial mathematics and model development.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your academic background and practical experience align with the requirements of the PhD Analyst position, particularly your knowledge of financial engineering and programming skills.
Showcase Your Technical Skills: Include a section in your application that specifically outlines your programming skills in Python and C++. Mention any projects or experiences where you developed numerical algorithms or worked on risk models, as this is crucial for the role.
Proofread Your Application: Before submitting, carefully proofread your application materials. Ensure there are no grammatical errors and that your writing is clear and concise. Good written communication is essential for this position, so make a strong first impression.
How to prepare for a job interview at Morgan McKinley
✨Showcase Your Technical Skills
Make sure to highlight your programming skills in C++ and Python during the interview. Be prepared to discuss specific projects or experiences where you applied these skills, especially in relation to quantitative models or financial products.
✨Demonstrate Your Mathematical Knowledge
Since the role requires a strong mathematical background, be ready to explain complex concepts in financial mathematics. You might be asked to solve problems on the spot, so brush up on relevant theories and applications.
✨Communicate Clearly
Good communication is key, especially when liaising with different teams. Practice explaining technical concepts in simple terms, as you may need to convey your ideas to non-technical stakeholders.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about how you would approach deploying a risk model to production and be ready to discuss your thought process and decision-making.
