Machine Learning Scientist

About the position

The Applied Innovation of AI (AI2) team is an elite machine learning group strategically located within the Chief Technology Office of JP Morgan Chase. AI2 tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for Software, Cybersecurity and Technology Infrastructure. The team partners closely with stakeholders in these areas to execute projects that require machine learning development to support JPMC businesses as they grow. Strategically positioned in the Chief Technology Office, our work spans across Cybersecurity, Global Technology Infrastructure and the Software Development Lifecycle (SDLC). With this unparalleled access to technology groups in the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates. As a Machine Learning Scientist, you will apply sophisticated machine learning methods to a wide variety of complex tasks including data mining and exploratory data analysis and visualisation, text understanding and embedding, anomaly detection in time series and log data, large language models (LLMs) and generative AI for technology use-cases, reinforcement learning and recommendation systems. You must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. You must also have a passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

Responsibilities

• Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation
• Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
• Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
• Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
• Contribute to reusable code and components that are shared internally and also externally

Requirements

• PhD in a quantitative discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.) with 1 year experience Or Masters with 2 years of industry or research experience in the field.
• Hands-on experience and solid understanding of machine learning and deep learning methods
• Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine-tuning)
• Scientific thinking and the ability to invent
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Experience with big data and scalable model training
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
• Curious, hardworking and detail-oriented, and motivated by complex analytical problems
• Ability to work both independently and in highly collaborative team environments

Nice-to-haves

• Strong background in Mathematics and Statistics
• Familiarity with the financial services industries
• Experience with A/B experimentation and data/metric-driven product development
• Experience with cloud-native deployment in a large scale distributed environment
• Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
• Ability to develop and debug production-quality code
• Familiarity with continuous integration models and unit test development