At a Glance
- Tasks: Use AI/ML to solve complex business problems and optimise decision-making.
- Company: Join J.P. Morgan's innovative Quantitative Trading & Research team.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on diversity and inclusion.
- Why this job: Make a real impact by transforming business processes with cutting-edge technology.
- Qualifications: Advanced degree in a quantitative field and strong Python skills required.
The predicted salary is between 50000 - 70000 £ per year.
Quantitative Trading & Research (QTR) is an expert group in J.P. Morgan specializing in statistical modelling, data analytics, balance sheet optimization and other quantitative methods to support the Commercial and Investment Bank (CIB). The QTR Securities Services, Payments and CIB Treasury team applies cutting‑edge AI/ML techniques to fundamentally transform the way we do business by enabling business revenue growth and efficiency within the CIB organization.
As an Associate/ Vice President in the Quantitative Trading & Research (QTR) Securities Services, Payments and CIB Treasury team, you will support the business in tackling their most technically complex business problems. This could range from leveraging LLMs to deliver capabilities at scales never before possible, to developing ML applications that make business‑critical predictions, to handling vast data sets using J.P. Morgan's Cloud capabilities. You will be in tight partnership with the business in identifying their most pressing pain points and iterating towards a solution that really works for them. If you are passionate about solving real‑world problems using your quantitative background and experience, this may be just the team for you.
Job responsibilities
- Work with business leads to develop AI/ML‑driven analytics and automation that support their business goals
- Build up a scalable data architecture to handle the large volume of transaction data
- Automate existing manual processes and build tools to enable the business to optimize their decision making and deposit management
- Build sequential decision making tools to optimize the net interest income of the business under various liquidity, capital and balance sheet constraints
- Drive projects end‑to‑end, from brainstorming, prototyping, data processing, data analysis to model development
- Make real‑world, commercial recommendations through effective presentations to various stakeholders
- Leverage data visualization to communicate quantitative insights to help business decision‑making
- Work closely with colleagues in Quantitative Research, CIB Treasury, Chief Investment Office, Technology and the Chief Data and Analytics Office (CDAO) to drive the CIB data strategy forward
Required qualifications, capabilities, and skills
- Advanced degree (PhD or MS) or equivalent in a quantitative field: Physics, Mathematics, Computer Science, Engineering, etc.
- Robust understanding of Machine Learning, Statistics, and Mathematics, both in fundamentals as well as in application
- Experience in tackling real world data science problems, end‑to‑end from prototype to production, using Python
- Excellent communication skills (both verbal and written) and the ability to present findings to a non‑technical audience
- Passion for learning, sharing knowledge, building collaborations, and getting things done
Preferred qualifications, capabilities, and skills
- Participation in KDD/Kaggle competition, Hackathons or contribution to GitHub
- You demonstrate hands‑on experience in solving sequential decision making problems
- Experience in applying LLMs and/or deep learning methods to solve business problems
- Experience in working with Cloud and/or HPC environments
Equal Opportunity Employment
We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants and employees' religious practices and beliefs, as well as mental health or physical disability needs.
Quantitative Trading & Research â Securities Services, Payments and CIB Treasury â Associate/Vi[...] employer: 慨正橡扯
J.P. Morgan is an exceptional employer, offering a dynamic work environment where innovation meets collaboration. As part of the Quantitative Trading & Research team, you will engage in cutting-edge AI/ML projects that not only enhance your technical skills but also contribute to meaningful business solutions. With a strong commitment to diversity and inclusion, along with ample opportunities for professional growth, J.P. Morgan provides a supportive culture that empowers employees to thrive in their careers.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Trading & Research â Securities Services, Payments and CIB Treasury â Associate/Vi[...]
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at J.P. Morgan or similar firms. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your projects, especially those involving AI/ML and data analytics. When you get the chance to chat with recruiters, let your work speak for itself.
✨Tip Number 3
Practice makes perfect! Get comfortable discussing complex topics like machine learning and quantitative methods. You never know when you'll need to explain your thought process in an interview.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Trading & Research â Securities Services, Payments and CIB Treasury â Associate/Vi[...]
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the job description. Highlight your quantitative background and any relevant projects you've worked on, especially those involving AI/ML techniques.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about solving real-world problems in quantitative trading and research. Share specific examples of how you've tackled complex data science challenges in the past.
Showcase Your Communication Skills:Since you'll be presenting findings to non-technical audiences, it's crucial to demonstrate your ability to communicate complex ideas clearly. Use concise language and avoid jargon in your application materials.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother application process. This way, we can ensure your application gets the attention it deserves!
How to prepare for a job interview at 慨正橡扯
✨Know Your Quantitative Stuff
Make sure you brush up on your knowledge of machine learning, statistics, and mathematics. Be ready to discuss how you've applied these concepts in real-world scenarios, especially using Python. This will show that you can tackle complex problems effectively.
✨Showcase Your Problem-Solving Skills
Prepare examples of projects where you've taken a problem from prototype to production. Highlight your experience with sequential decision-making tools and how they optimised business outcomes. This will demonstrate your hands-on experience and ability to drive projects end-to-end.
✨Communicate Like a Pro
Practice explaining your findings to a non-technical audience. Use clear, concise language and consider using data visualisation techniques to make your points more impactful. This will help you connect with stakeholders and show that you can bridge the gap between technical and business needs.
✨Be Ready to Collaborate
Since you'll be working closely with various teams, think about how you've successfully collaborated in the past. Be prepared to discuss your experiences in building partnerships and sharing knowledge, as this is crucial for driving the CIB data strategy forward.