
Global financial services firm
Applied AI & Machine Learning Associate – Markets Operations
Required skills
Python
PyTorch
TensorFlow
Spark
Machine Learning
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 & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.
300,000+
Employees
New York City
Headquarters
$500B
Valuation
Reviews
10 reviews
3.8
10 reviews
Work-life balance
3.5
Compensation
4.0
Culture
3.8
Career
3.2
Management
2.8
68%
Recommend to a friend
Pros
Good benefits and compensation
Supportive colleagues and environment
Flexible work arrangements
Cons
Long hours and heavy workload
Management issues and lack of direction
High stress and expectations
Salary Ranges
44 data points
Mid/L4
Senior/L5
Mid/L4 · Applied AI ML Associate
2 reports
$188,500
total per year
Base
$145,000
Stock
-
Bonus
-
$182,000
$195,000
Interview experience
4 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Offer rate
50%
Experience
Positive 25%
Neutral 75%
Negative 0%
Interview process
1
Application Review
2
HR Screen
3
Hiring Manager Interview
4
In-person/Final Interview
5
Offer
Common questions
Behavioral/STAR
Past Experience
Culture Fit
Financial Knowledge
Case Study
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