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- Tasks: Develop cutting-edge quantitative credit models and enhance alpha research across various credit markets.
- Company: Global asset manager with a strong focus on systematic credit capabilities.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Other info: Engage with diverse teams and contribute to innovative research in a fast-paced industry.
- Why this job: Make a real impact in finance by blending research and implementation in a collaborative setting.
- Qualifications: BSc/MSc in a quantitative field; experience in building financial models and strong Python skills.
The predicted salary is between 60000 - 80000 £ per year.
Our client is a global asset manager with a leading systematic credit capability. They seek a Quantitative Analyst to enhance alpha research, modelling and the research platform across investment grade, high yield and structured products. The role blends research, implementation and close partnership with portfolio teams.
Responsibilities
- Develop quantitative credit models, including spread and default risk frameworks.
- Source, test and integrate new alpha signals across credit markets into research pipelines.
- Monitor model-generated trade ideas and disseminate outputs to investment teams.
- Produce rigorous standalone research with clear reports and presentations.
- Contribute thematic quantitative research and thought leadership across credit.
- Build and maintain the research platform and shared analytical codebase.
- Improve communication and visualisation of model outputs for portfolio teams.
- Uphold code quality, version control and systematic testing standards.
- Translate quantitative insights for Portfolio Managers, Credit Analysts, Traders and Investment Directors.
- Engage in cross-asset forums and collaborate across regions and disciplines.
- Support the internal and external profile of the quantitative research function.
- Discuss model methodology and research outputs with clients when required.
Requirements
- BSc/MSc in a quantitative discipline; strong statistics, econometrics and numerical methods.
- Proven track record building quantitative models in an investment or credit context.
- Strong Python (pandas, NumPy); SQL and Git advantageous.
- Advanced Excel; familiarity with Bloomberg or similar data platforms.
- Experience handling large, complex financial datasets.
- Clear written and verbal communication of technical concepts.
- Collaborative and adaptable, comfortable working across functions and asset classes.
Quantitative Analyst – Credit, Global Asset Manager employer: Logansinclair
Contact Detail:
Logansinclair Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Analyst – Credit, Global Asset Manager
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Prepare for interviews by practising common questions and showcasing your quantitative skills. We recommend running through some mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 3
Showcase your projects! If you've built any models or conducted research, make sure to have them ready to discuss. We love seeing real examples of your work that demonstrate your skills and thought process.
✨Tip Number 4
Don’t forget to apply through our website! It’s a great way to ensure your application gets seen. Plus, we’re always looking for talented individuals who are passionate about quantitative analysis and credit markets.
We think you need these skills to ace Quantitative Analyst – Credit, Global Asset Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Quantitative Analyst role. Highlight your experience with quantitative models, Python, and any relevant projects that showcase your skills in credit analysis. We want to see how you fit into our world!
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. Be sure to mention any specific experiences that relate to the responsibilities listed in the job description.
Showcase Your Technical Skills: Don’t forget to highlight your technical skills, especially in Python, SQL, and Excel. If you've worked with large datasets or have experience in model development, make that clear! We love seeing candidates who can demonstrate their technical prowess.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates. Plus, it’s super easy!
How to prepare for a job interview at Logansinclair
✨Know Your Models Inside Out
Make sure you can discuss the quantitative credit models you've built in detail. Be prepared to explain your methodology, the data you used, and how you validated your results. This shows not only your technical skills but also your ability to communicate complex concepts clearly.
✨Brush Up on Python and SQL
Since strong Python skills are essential for this role, practice coding problems related to data manipulation and analysis. Familiarise yourself with libraries like pandas and NumPy. If you have experience with SQL, be ready to discuss how you've used it to handle large datasets in previous projects.
✨Prepare for Technical Questions
Expect questions that test your understanding of statistics, econometrics, and numerical methods. Review key concepts and be ready to solve problems on the spot. This will demonstrate your analytical thinking and problem-solving abilities, which are crucial for a Quantitative Analyst.
✨Showcase Your Communication Skills
Since you'll need to translate quantitative insights for various stakeholders, practice explaining your research and findings in simple terms. Prepare examples of how you've effectively communicated complex ideas in the past, whether through reports or presentations.