Our Time Series & Reinforcement Learning team is a passionate group of academics and ML engineers building AI inductive reasoning systems across the firm. In leading ML science in the “Derivatives House of the Year 2022” (Risk Magazine Award), this team works closely in exploring cutting-edge research and applying the latest Machine Learning techniques to J.P. Morgan’s unique data assets. Our work spans the company’s lines of business, with exceptional opportunities in each.
As a Machine Learning Scientist in the Time Series Reinforcement Learning team, you will apply sophisticated machine learning methods to banking applications including risk assessment, trading models, customer relationship management, and pricing models. Machine learning techniques will include feed-forward, recurrent, recursive and convolutional neural networks, maximum entropy models, and other algorithms related to time series analysis and supervised learning. We will rely on your research and work experience to help us implement intelligent and practical algorithms at scale. You will have a deep understanding of the various techniques, models and cutting edge practices in machine learning and will have insight into what works best in real-world situations. You will be at the centre of prescribing, designing and building mission-critical solutions.
Job responsibilities
Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community Develop state-of-the art machine learning models to solve real-world problems and apply them to tasks such as time-series predictions, market modelling, and decision optimization Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
Required qualifications, capabilities and skills
Ph.D. or in the last year of a Ph.D. program in machine learning, statistics, mathematics, computer science, economics, finance, science, engineering, or other quantitative fields Knowledge of machine learning / data science theory, techniques, and tools Scientific thinking, ability to work with literature and the ability to implement complex projects Ability to understand business problems, study literature for a solution approach, write high quality code for the chosen method, design training and experimentation to validate the algorithms and implementation, and to evaluate intrinsic and extrinsic metrics for model performance aligned with business goals Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences Ability to work both independently and in highly collaborative team environments Excellent analytical, quantitative, and problem-solving skills and demonstrated research ability Curious, hardworking, detail-oriented and motivated by complex analytical problems
Preferred qualifications, capabilities and skills
Knowledge of Financial Mathematics, Stochastic Calculus, Bayesian techniques, Statistics, State-Space models, MCMC, DSGE models, MCTS / distributed compute, NLP, accounting Knowledge and experience with Reinforcement Learning methods Knowledge of Python, Tensorflow, tf-agent, Ray, RLLib, Tune, or other ML frameworks Experience with any of OOP, graph-based computation engines, large scale software development, C++/Java/CUDA, performance focused implementations, numerical algorithms, distributed computing, cloud computing, data transformation pipelines Familiarity with continuous integration models and unit test development Published research in areas of natural language processing, speech recognition, reinforcement learning, or deep learning at a major conference or journal Strong passion for machine learning and habits to invest independent time towards learning, researching, and experimenting with innovations across a variety of fields
About MLCOE
The Machine Learning Center of Excellence (MLCOE) team partners across the firm to create and share Machine Learning Solutions for our most challenging business problems. In this role you will work and collaborate with a team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning
For more information about the MLCOE, please visit http://www.jpmorgan.com/mlcoe. To learn about how we're using AI/ML to drive transformational change, please read this blog: https://www.jpmorgan.com/insights/technology/technology-blog?source=cib_di_jp_aBtechblog102
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.