招聘

Applied AI & Machine Learning Associate – Markets Operations
LONDON, LONDON, United Kingdom, GB
·
On-site
·
Full-time
·
4mo ago
Join us to shape the future of banking through cutting-edge AI and machine learning. You’ll collaborate with a dynamic team of data scientists, engineers, and product managers to create impactful products for our operations teams. This is your opportunity to work on unique financial datasets and deliver solutions that make a measurable difference. We value your curiosity and passion for both theory and hands-on development. Discover career growth and the chance to influence how banking is done.
As an Applied AI & Machine Learning Associate supporting Markets Operations, you will design, develop, and deploy machine learning products that enhance our corporate and investment banking services. You’ll work closely with cross-functional teams to deliver scalable solutions and drive operational transformation. Your contributions will directly impact how we serve clients and manage behind-the-scenes operations. We foster a collaborative environment where your ideas and expertise are valued.
Job Responsibilities:
- Research and develop innovative machine learning solutions for complex operational challenges
- Build robust data science capabilities scalable across multiple business use cases
- Collaborate with software engineering teams to design and deploy machine learning services
- Analyze large financial datasets using statistical and machine learning techniques
- Communicate AI capabilities and results to technical and non-technical audiences
- Document methodologies, techniques, and processes
- Write production-ready code and ensure solutions are deployable at scale
- Develop products that transform corporate and investment banking operations
- Work in agile, cross-functional teams to deliver impactful solutions
Required Qualifications, Capabilities, and Skills:
- Master’s degree in a quantitative or computational discipline
- Hands-on experience developing and deploying data science and machine learning capabilities in production
- Proficiency in Python development, debugging, and maintenance
- Experience with Natural Language Processing (NLP)
- Familiarity with machine learning frameworks (e.g., Py Torch, Tensor Flow) and data science packages (e.g., Scikit-Learn, Num Py, Sci Py, Pandas, statsmodels)
- Ability to work independently and collaboratively
- Strong attention to detail and interest in analytical problem-solving
- Results-driven mindset with a client focus
- Ability to thrive in agile, cross-functional teams
Preferred Qualifications, Capabilities, and Skills:
- Ability to design model evaluations aligned with business goals
- Experience partnering with non-specialists and building stakeholder trust
- Experience with inference, training, and deployment of Large Language Models
- Experience building generative AI solutions
- Experience developing scalable machine learning systems
- Familiarity with big-data technologies such as Spark
#CIBAppliedAI
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About JPMorgan Chase

JPMorgan Chase
PublicJPMorgan Chase is a multinational investment bank and financial services company that provides banking, investment, and asset management services globally. It is one of the largest banks in the United States by assets and market capitalization.
300,000+
Employees
New York City
Headquarters
Reviews
4.2
10 reviews
Work Life Balance
4.2
Compensation
4.3
Culture
4.5
Career
4.4
Management
4.1
75%
Recommend to a Friend
Pros
Good pay and benefits
Work-life balance
Career advancement opportunities
Cons
Heavy workload at times
Career advancement takes time
Pay could be better in some roles
Salary Ranges
47 data points
Mid/L4
Senior/L5
Mid/L4 · Applied AI ML Associate
2 reports
$188,500
total / year
Base
$145,000
Stock
-
Bonus
-
$182,000
$195,000
Interview Experience
4 interviews
Difficulty
2.8
/ 5
Duration
14-28 weeks
Interview Process
1
Application Review
2
HireVue Video Interview
3
Technical/Behavioral Assessment
4
Final Interview Round
5
Offer Decision
Common Questions
Behavioral/STAR
Technical Knowledge
Past Experience
Culture Fit
Case Study
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