At a Glance
- 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.
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.
Qualifications
- 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
- 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 in City of Westminster employer: Jpmorgan Chase & Co.
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Asset Management, Equity Quant Researcher, Associate in City of Westminster
✨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 expertise in Python and how you've tackled complex datasets. Make sure to highlight any reinforcement learning techniques you've used!
✨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 explaining your research findings and be ready to discuss how you can contribute to a collaborative team environment.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for fresh talent in quantitative research. Keep an eye on our job postings and make sure your application stands out by tailoring it to the specific role and showcasing your relevant experience.
We think you need these skills to ace Asset Management, Equity Quant Researcher, Associate in City of Westminster
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your programming skills in Python and any experience you have with machine learning libraries. We want to see how you can apply these skills to tackle complex datasets and improve forecasting models.
Be Clear and Concise: When presenting your research or past projects, keep it straightforward. We appreciate candidates who can explain complex ideas clearly, so think about how you can make your application easy to understand for both technical and non-technical folks.
Tailor Your Application: Don’t just send a generic application! Make sure to tailor your CV and cover letter to reflect the specific responsibilities and qualifications mentioned in the job description. Show us why you’re the perfect fit for this role!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process.
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your Data
Make sure you’re well-versed in the types of data sources mentioned in the job description. Brush up on 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, as well as the methodologies you used. This will show your ability to bridge the gap between quantitative research and investment decisions.
✨Collaborate and Innovate
Highlight your experience working in teams and your approach to continuous learning. Be prepared to discuss how you’ve contributed to collaborative environments and any innovative solutions you’ve developed in your previous roles.
