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- Tasks: Research and develop innovative alpha signals to enhance investment models.
- Company: Leading asset management firm focused on quantitative finance.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Join a dynamic team and make an impact in the world of finance with cutting-edge technology.
- Qualifications: PhD in a quantitative field and strong programming skills in Python.
- Other info: Collaborative environment with a focus on continuous learning and innovation.
The predicted salary is between 36000 - 60000 £ per year.
Job responsibilities:
- Research and develop novel alpha signals using traditional and alternative data sources to enhance return forecasting models.
- Improve return forecasting models and portfolio construction frameworks for global equity markets, applying reinforcement learning and advanced machine learning techniques.
- Apply statistical, econometric, and machine learning methods to large, complex datasets to extract actionable insights.
- Collaborate with technology teams to integrate research models into production systems and ensure robust implementation.
- Partner with portfolio managers and stakeholders to translate quantitative research into investment decisions.
- Stay current with academic and industry developments in quantitative finance, machine learning, and data science.
- Present complex research findings clearly to both technical and non-technical audiences.
- Contribute to a collaborative team environment and support continuous learning and innovation.
Required qualifications, capabilities, and skills:
- PhD in machine learning, computer science, statistics, or a related quantitative discipline; specialization in reinforcement learning highly desirable.
- 0–3 years of experience in quantitative research, data science, or a related field (industry or academic).
- Strong programming skills in Python and experience with machine learning libraries.
- Familiarity with quantitative modeling, portfolio construction, and equity markets.
- Experience working with large, complex, and alternative datasets.
- Excellent verbal and written communication skills, with the ability to present complex ideas to technical and non-technical audiences.
- Demonstrated ability to work effectively in a team environment.
- Strong problem-solving skills, intellectual curiosity, and ability to drive research projects independently.
Preferred qualifications, capabilities, and skills:
- Experience integrating research models into production investment systems.
- Background in developing and implementing reinforcement learning techniques in finance.
- Experience collaborating with portfolio managers and technologists.
- Track record of publishing or presenting research in quantitative finance or machine learning.
Asset Management, Equity Quant Researcher, Associate employer: J.P. Morgan
Contact Detail:
J.P. Morgan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Asset Management, Equity Quant Researcher, Associate
✨Tip Number 1
Network like a pro! Reach out to professionals in the asset management and quantitative finance space. Use platforms like LinkedIn to connect with people who work at companies you're interested in. A friendly chat can sometimes lead to job opportunities that aren't even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and data analysis. This is your chance to demonstrate your programming prowess in Python and how you've tackled complex datasets. Make it easy for potential employers to see what you can do!
✨Tip Number 3
Prepare for interviews by brushing up on your communication skills. You’ll need to explain complex concepts clearly to both technical and non-technical audiences. Practice presenting your research findings and be ready to discuss how your work can translate into investment decisions.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of resources to help you land that dream job. Plus, applying directly shows your enthusiasm and commitment to joining our team. Let’s get you started on this exciting journey together!
We think you need these skills to ace Asset Management, Equity Quant Researcher, Associate
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in quantitative research and machine learning. We want to see how your skills align with the job responsibilities, so don’t be shy about showcasing your programming prowess in Python and any projects you've worked on!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about asset management and how your background makes you a great fit for the role. We love seeing enthusiasm and a clear understanding of the position, so let your personality come through.
Showcase Your Projects: If you've worked on any interesting projects or research, make sure to mention them! Whether it's developing alpha signals or using reinforcement learning techniques, we want to know how you've applied your skills in real-world scenarios. Include links if possible!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you’re genuinely interested in joining our team at StudySmarter!
How to prepare for a job interview at J.P. Morgan
✨Know Your Data
Make sure you’re well-versed in the types of data sources mentioned in the job description. Brush up on both traditional and alternative datasets, and be ready to discuss how you’ve used them in your past work or research.
✨Showcase Your Programming Skills
Since strong programming skills in Python are crucial, prepare to demonstrate your proficiency. Bring examples of projects where you’ve applied machine learning libraries, and be ready to solve a coding challenge during the interview.
✨Communicate Clearly
You’ll need to present complex ideas to both technical and non-technical audiences. Practice explaining your research findings in simple terms, and think about how you can make your insights accessible to everyone involved.
✨Collaborate and Innovate
Highlight your experience working in teams and your ability to contribute to a collaborative environment. Be prepared to discuss how you’ve partnered with others in the past, especially in translating quantitative research into actionable investment decisions.
