The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey, leveraging artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management.
Requirements
- Solid background in NLP or speech recognition and analytics, personalization/recommendation and 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)
- 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 and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
- Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
- Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
- Knowledge in search/ranking, Reinforcement Learning or Meta Learning
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 it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems
- Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
- Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Other
- PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or an MS with at least 3 years of industry or research experience in the field.
- 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
- Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
- Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
