Join the Applied Innovation of AI team, a premier machine learning group within the Chief Technology Office of JP Morgan Chase. We tackle crucial business priorities using innovative machine learning techniques, focusing on Software, Cybersecurity, and Technology Infrastructure. 

 

As an Applied AI ML Lead - Senior Machine Learning Scientist within the Applied Innovation of AI (AI2) team, you will apply sophisticated machine learning methods to a wide variety of complex tasks, collaborate closely with stakeholders, and invest independent time towards learning, researching, and experimenting with new innovations in the field. This role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.

 

Job 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 ext

 

Required qualifications, capabilities and skills

PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science. Or an MS with full time 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) 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

 

Preferred qualifications, capabilities and skills

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 Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models) Knowledge in Reinforcement Learning or Meta Learning 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