2022 AI & Data Science Associate Program – Off-Cycle Internship

J.P. Morgan · Competitive · London
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What to expect

Our Off-Cycle Associate Internship Program begins in Spring, Summer or Autumn, depending on your academic schedule. Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, engaging speaker series with senior leaders and more. Full-time employment offers may be extended upon successful completion of the program.

Our AI and Data Science teams work closely together, whether exploring cutting-edge research in partnership with leading academic institutions, applying the latest Machine Learning techniques to J.P. Morgan’s unique data assets, or collaborating directly with traders and salespeople to drive the data-led transformation of our businesses.

Our areas of focus include Deep Learning, Reinforcement Learning, Natural Language Processing, Speech/Voice Analytics, Time Series, Computer Vision, Cryptography, Explainability and Interpretability, and Ethics and Fairness of AI.

About you
We are looking for innovative problem-solvers with a passion for developing complex solutions that support our global business.

Key qualifications include:  

  • Enrolled in a Ph.D. or 18-24-month Master’s* degree program in math, science, engineering, computer science or other quantitative fields
  • Knowledge of machine learning / data science theory, techniques and tools
  • Programming experience with one or more of Python, Matlab, C++, Java, C#
  • Excellent analytical, quantitative and problem solving skills and demonstrated research ability
  • Strong communication skills and the ability to present findings to a non-technical audience
  • No prior experience in financial markets required

* If you are pursuing a 1-year Master’s program, we encourage you to apply for the AI & Data Science Analyst program.

Desirable skills include:

  • Experience with big-data technologies such as Hadoop, Tensorflow, Spark, SparkML, et al.
  • Knowledge in Reinforcement Learning or Meta Learning

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