Data Scientist Associate Senior - AI / ML

Job Description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Data Scientist Senior Associate within our Business Banking Data and Analytics Team, you will be instrumental in constructing predictive models and developing robust RAG pipelines. Collaborating closely with cross-functional teams, you will extract valuable insights from complex datasets to promote data-driven decision-making across the organization. Your focus will include addressing problem statements and innovating solutions for complex challenges. Additionally, you will build AI-based solutions to enhance both technological and business efficiency.

Job Responsibilities

• Align ML problem definition with business objectives to ensure solutions address real-world needs.
• Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.
• Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.
• Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.
• Promote software and model quality, integrity, and security throughout the organization.
• Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.
• Integrate GenAI solutions with enterprise platforms using API-based methods.
• Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.
• Collaborate with technology teams to lead the design and delivery of GenAI products.

Required Qualifications, Capabilities And Skills

Preferred Qualifications, Capabilities And Skills

ABOUT US

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. 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. Visit our FAQs for more information about requesting an accommodation.

About The Team

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

Skills Required

Machine Learning, Data Structures, Data Mining, Statistics, Tensorflow, Algorithms, Rdbms, Pytorch, Information Retrieval, Keras