You thrive on diversity and creativity, and we welcome individuals who share our vision of making a lasting impact. Your unique combination of design thinking and experience will help us achieve new heights.

  As a Data Scientist Associate at JPMorgan Chase within the Asset & Wealth Management, you are part of an agile team that works to enhance, design, and deliver the data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As an emerging member of a data engineering team, you execute data solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities

 

Organizes, updates, and maintains gathered data that will aid in making the data actionable. Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives. Demonstrates basic knowledge of the data system components to determine controls needed to ensure secure data access. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Responsible for making custom configuration changes in one to two tools to generate a product at the business or customer request Updates logical or physical data models based on new use cases with minimal supervision 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 2+ 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. Basic knowledge of data system components to determine controls needed 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.