Machine Learning Scientist - Time Series Reinforcement Learning - Vice President - Machine Lear[...]

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.

The Job

The successful candidate 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.

You will be called upon to draw from your research and work experience to help us implement intelligent and practical algorithms at scale. The ideal candidate 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.

We're looking for humble, enthusiastic, bright and personable people with strong communication skills and a deep knowledge of machine learning. We need a proven track record in innovation with strong potential for growth into a leadership position.

Responsibilities

1. Develop scalable tools leveraging machine learning and deep learning models to solve real-world problems for various problems related to finance, economics and operations of JP Morgan.
2. Collaborate with all of JPMorgan Chase's lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.
3. Lead your own project. Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level machine learning application.

Qualifications

1. PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science
2. For VP position, several years of working experiences
3. Experiences in machine learning project development
4. Knowledge of machine learning / data science theory, techniques, and tools
5. Scientific thinking, ability to work with literature and the ability to implement complex projects
6. Willingness to understand business problem, 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
7. Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
8. Curious, hardworking, detail-oriented and motivated by complex analytical problems

Beneficial Skills

1. Solid time series analysis, machine learning or financial engineering background.
2. Strong background in Mathematics and Statistics.
3. Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.
4. Experience designing and performance tuning large scale distributed systems.
5. Contribution to open-source projects on Machine Learning.
6. Knowledge in Reinforcement Learning or Meta Learning.
7. Experience with frameworks for distributed machine learning such as Ray, etc.

About MLCOE
The Machine Learning Center of Excellence (MCLOE) 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.