Machine Learning Scientist – ML/AI for Software, Cybersecurity and Technology
Location: Greater London
Job Type: Full time
The Applied Innovation of AI (AI2) team is an elite machine learning group strategically located within the CTO office of JP Morgan Chase. AI2 tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for Software, Cybersecurity and Technology Infrastructure. The team partners closely with all lines of business and engineering teams across the firm to execute long-term projects in these areas that require significant machine learning development to support JPMC businesses as they grow.
Create innovative machine learning solutions to solve business critical problems. Develop software and application system prototypes of the proposed solutions. Conduct experiments to evaluate the performance and effectiveness of the solutions. Create proof-of-concept technology demonstrations. Potentially generate creative solutions (patents) and publish research results internally and in some cases where permissible external events.
To be successful in this role, one most important requirements is the innate curiosity for what's new and a strong ability to learn new technologies quickly. The individual must possess deep, hands-on technical skills and be familiar with a wide range of technical themes and development practices. They will contribute as technical subject matter experts to the various machine learning projects, out-of-box thinking and team work is also critical for this role.
- 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 it 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 also externally
- 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 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
- 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