Applied AI ML Lead - Sales Science

Job responsibilities

• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Build and enhance ML workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Conducting experiments using latest ML technologies, analyzing results, tuning models.
• Actively engage in hands-on coding to convert experimental results into robust production solutions.
• Take full ownership of the entire code development lifecycle in Python, from proof of concept and experimentation to delivering production-ready solutions.
• Integrate Generative AI within the ML Platform using state-of-the-art techniques.

Required qualifications, capabilities, and skills

• Bachelor's degree with 7 years of applied machine learning experience.
• 5+ years of experience in one of the programming languages like Python, R, Java, etc. Intermediate Python is a must.
• Experience in applying data science, ML techniques to solve business problems.
• Solid background in Natural Language Processing (NLP) and Large Language Models (LLMs).
• Experience with machine learning and deep learning methods.
• Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow.
• Ability to work on tasks and projects through to completion with limited supervision.
• Passion for detail and follow through. Excellent communication skills and team player.

Preferred qualifications, capabilities, and skills

• In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
• MS and/or PhD in Computer Science, Machine Learning, or a related field, with at least 5 years of applied machine learning experience preferred.
• Advanced knowledge in Reinforcement Learning or Meta Learning.
• Software development experience is a plus.
• Demonstrated ability to translate LLM pipelines/workflows into something less technical business partners can understand.
• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
• Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc.

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.