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JPMorgan Data Science Director 
United States, Texas, Plano 
91139071

26.06.2024

As a Data Scientist Director in the ISDA team, you will have the opportunity to work on exciting end-to-end machine learning problems and solutions. You will be tasked with solving large scale Corporate Technology use cases and engineering scalable features. Additionally, you will be comfortable doing ML Ops work to build brand new systems. This role provides a unique opportunity to apply your skills in a dynamic and innovative environment.

Job Responsibilities:

  • Lead complex projects that require cross-organizational alignment and collaboration
  • Analyze complex datasets used to make decisions regarding real-world applications
  • Investigate the applicability of new approaches to business problems and products by combining theory and experimentation
  • Write software code that is built and deployed in production systems
  • Anticipate and consider risks when building machine learning solutions and prediction/classification systems
  • Communicate complex issues clearly and credibly to consult on and approve team decisions while driving broader organizational actions.
  • Work transparently cross-functionally and influence your peers to do the same.

Required qualifications, capabilities and skills:

  • Ph.D. in Computer Science or Machine Learning related degree and about 10 years of work experience
  • Expert in one or more of the following: machine learning, generative AI, recommendation systems, network analysis, Natural language processing, Reinforcement learning, MLOps
  • Comfortable conducting design and code reviews
  • Experience with leadership of experienced scientists as well as a record of developing junior members from academia/industry to a career track in a business environment

Preferred qualifications, capabilities and skills:

  • A strong technical advocate with a background in Java or Scala, and Python.
  • Comfortable working in a cloud environment like AWS/GCP or Azure
  • Passionate about working with large unstructured and structured datasets