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- Tasks: Develop predictive models and research market inefficiencies in fixed income.
- Company: Leading global investment firm based in London.
- Benefits: Competitive salary, dynamic work environment, and opportunities for growth.
- Why this job: Join a top firm and make an impact in financial markets with your research.
- Qualifications: Strong academic background in quantitative fields and experience with Python, R, or C++.
- Other info: Collaborate with technologists in a fast-paced, innovative setting.
The predicted salary is between 36000 - 60000 £ per year.
A leading global investment firm in London is seeking a Quantitative Researcher to focus on fixed income research. This role involves developing predictive models, conducting research to uncover market inefficiencies, and collaborating with technologists.
Ideal candidates should have a strong academic background in quantitative disciplines and experience with Python, R, or C++.
Applications can be sent to sean@cwtalentsolutions.com.
Fixed Income Quant Researcher: Build & Backtest Signals in London employer: CW Talent Solutions
Contact Detail:
CW Talent Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Fixed Income Quant Researcher: Build & Backtest Signals in London
✨Tip Number 1
Network like a pro! Reach out to professionals in the fixed income space on LinkedIn or at industry events. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and research projects. This is a great way to demonstrate your expertise in Python, R, or C++ and make you stand out.
✨Tip Number 3
Prepare for interviews by brushing up on your quantitative skills and market knowledge. Be ready to discuss your past projects and how they relate to uncovering market inefficiencies.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Fixed Income Quant Researcher: Build & Backtest Signals in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your quantitative skills and any relevant experience in fixed income research. We want to see how your background aligns with the role, so don’t be shy about showcasing your Python, R, or C++ expertise!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about quantitative research and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Projects: If you've worked on any projects related to predictive modelling or market inefficiencies, make sure to mention them. We appreciate candidates who can demonstrate their practical experience and problem-solving skills.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves!
How to prepare for a job interview at CW Talent Solutions
✨Know Your Quantitative Stuff
Make sure you brush up on your quantitative skills and theories relevant to fixed income research. Be prepared to discuss your academic background and any projects you've worked on that showcase your ability to develop predictive models.
✨Show Off Your Coding Skills
Since the role requires experience with Python, R, or C++, be ready to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice coding challenges beforehand to boost your confidence.
✨Understand Market Inefficiencies
Research common market inefficiencies in fixed income markets and think about how you would approach uncovering them. Having specific examples or case studies in mind will show your depth of understanding and analytical thinking.
✨Collaboration is Key
This role involves working closely with technologists, so be prepared to discuss your experience in collaborative environments. Think of examples where you successfully worked in a team to achieve a common goal, especially in a tech-driven context.
