Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

  As a Data Scientist Associate Senior at JPMorgan Chase within the Asset & Wealth Management , you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

 

Supports product managers, data scientists, ML engineers, and other stakeholders to understand requirements. Advises and makes custom configuration changes in one to two tools to generate a product at the business or customer request. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Updates and implements monitoring mechanisms to track model performance in real-time and ensure model reliability. Frequently conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Adds to team culture of diversity, equity, inclusion, and respect

 

Required qualifications, capabilities, and skills

 

Formal training or certification on Machine learning engineering concepts and 3+ years applied experience Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production. Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API. Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex. Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization. Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures. Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications. Experience customizing changes in a tool to generate product Preferred qualifications, capabilities, and skills Familiarity with the financial services industries. Expertise in designing and implementing AI/ML pipelines.   Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies, RAG. A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs.