Machine Learning Scientist – NLP & Recommender Systems – Associate / Vice President / Executive Director
The Machine Learning Center of Excellence is a world-class machine learning team continually advancing state-of-the-art methods to solve a wide range of real-world financial problems using the company’s vast and unique datasets.
The successful candidate will apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. The candidate must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.
Strategically positioned in the Chief Technology Office, our work spans across all of J.P. Morgan’s lines of business including Corporate & Investment Banking, Asset Wealth Management, Consumer & Community Banking, through every part of the organization from front office sales and trading, through to operations, technology, finance and more. With this unparalleled access to the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.
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 NLP, speech recognition, 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 Firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Minimum Qualifications
- [ED]: PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, with at least five years of industry or research experience in the field. Or an MS with at least eight years of experience.
- [VP]: PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science. Or an MS with at least three years of industry or research experience in the field.
- [Assoc]: MS in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science
- Solid background in personalization/recommendation, speech recognition or NLP
- 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
Beneficial Skills
- Strong background in Mathematics and Statistics
- Familiarity with the financial services industries
- Knowledge in personalized contents, recommender systems, and search/ranking
- Experience with A/B experimentation and data/metric-driven product development
- Experience with cloud-native deployment in a large scale distributed environment
- 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