Machine Learning Scientist – Time Series and Reinforcement Learning - Machine Learning Center of Excellence
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.
Explore the exciting world of Applied AI and Machine Learning with opportunities at Sr. Associate, Vice President, and Executive Director levels at our New York location. As part of the Machine Learning team at JPMorgan Chase, you`ll have the chance to utilize cutting-edge techniques and our unique data assets to optimize business decisions. Your role will involve applying sophisticated machine learning methods to various banking applications, including risk assessment, trading models, customer relationship management, and pricing models. With your deep understanding of machine learning techniques and models, you`ll be instrumental in implementing intelligent and practical algorithms, shaping mission-critical solutions.
As a Machine Learning Expert in the Time Series and Reinforcement Learning group, you will have the unique opportunity to apply sophisticated machine learning methods to various banking applications. You will be at the forefront of developing scalable tools leveraging machine learning and deep learning models to solve real-world problems related to finance, economics, and operations of JP Morgan. You will also have the chance to collaborate with all lines of businesses within JPMorgan Chase, leading your own project from conception to deployment of a production-level machine learning application.
Job Responsibilities
- 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.
- Collaborate with all of JPMorgan Chase`s lines of businesses, such as Investment Bank, Commercial Bank, and Asset Management.
- Lead your own project. Suggest, collect and synthesize requirements. Create an effective roadmap towards the deployment of a production-level machine learning application.
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science
- For VP position, minimum 3 years of working experiences
- Experiences in machine learning project development
- 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 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
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking, detail-oriented and motivated by complex analytical problems
Preferred qualifications, capabilities, and skills
- Solid time series analysis, speech recognition, NLP or financial engineering background.
- Strong background in Mathematics and Statistics.
- Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.
- Experience with GPUs and cloud-based training of deep neural networks.
- Contribution to open-source projects on Machine Learning.
- Knowledge in Reinforcement Learning or Meta Learning.
- Experience with big-data technologies such as Hadoop, Spark, SparkML, etc
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