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Machine Learning Scientist - ML/AI for Software, Cybersecurity and Technology

The Machine Learning Scientist will join the AI2 team within JPMorgan Chase's CTO office, focusing on developing innovative machine learning solutions for Software, Cybersecurity, and Technology Infrastructure. This role involves researching new ML methods, developing state-of-the-art models, and collaborating with various teams to deploy large-scale solutions into production. The ideal candidate will have strong hands-on experience in machine learning and deep learning toolkits.

About the role

About the Applied Innovation of AI (AI2) Team

The Applied Innovation of AI (AI2) team, an elite machine learning group within the CTO office of JP Morgan Chase, focuses on solving business-critical priorities using innovative machine learning techniques and technologies. Their work centers on machine learning for Software, Cybersecurity, and Technology Infrastructure. The team collaborates closely with various lines of business and engineering teams across the firm to execute long-term projects requiring significant machine learning development to support JPMC's growing businesses.

Responsibilities

Research and explore new machine learning methods through independent study, attending industry-leading conferences, and experimentation.
Develop state-of-the-art machine learning models to solve real-world problems and apply them to complex business-critical problems in Cybersecurity, Software, and Technology Infrastructure.
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.
Contribute to reusable code and components that are shared internally and externally.

Minimum Qualifications

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.
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 skills to effectively communicate technical concepts and results to both technical and business audiences.
Curious, hardworking, 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.
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.

About the role

About the Applied Innovation of AI (AI2) Team

Responsibilities

Research and explore new machine learning methods through independent study, attending industry-leading conferences, and experimentation.
Develop state-of-the-art machine learning models to solve real-world problems and apply them to complex business-critical problems in Cybersecurity, Software, and Technology Infrastructure.
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.
Contribute to reusable code and components that are shared internally and externally.

Minimum Qualifications

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.
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 skills to effectively communicate technical concepts and results to both technical and business audiences.
Curious, hardworking, 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.
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.