Applied AI ML Senior Associate - Machine Learning Scientist – Machine Learning for Technology

Greater London
Full time
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JP Morgan
Banking, investment & finance
10,001+ employees
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As an Applied AI ML Senior Associate - Machine Learning Scientist within the Applied Innovation of AI (AI2) team, you will have the opportunity to apply sophisticated machine learning methods to a wide variety of complex tasks. We tackle crucial business priorities using cutting-edge machine learning techniques, focusing on Software, Cybersecurity, and Technology Infrastructure. We collaborate closely with stakeholders to execute projects that require machine learning development to support the growth of JP Morgan Chase businesses. The successful candidate will apply advanced machine learning methods to a range of complex tasks and must have a strong passion for machine learning, solid expertise in Deep Learning, and a deep desire to learn. Our work spans across Cybersecurity, Global Technology Infrastructure, and the Software Development Lifecycle, offering a unique opportunity to tackle novel and complex challenges that could significantly transform how the bank operates.

Job 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 it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
  • Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure 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

Required qualifications, capabilities and skills

  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science. Or an MS with full time 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

Preferred qualifications, capabilities and 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 of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models)
  • 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