Applied AI Ml-executive Director

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Consumer & Community Banking

Executive Director for Applied AI/ML in AI for Operations at JPMorgan Chase, focusing on designing, building, and scaling cutting-edge NLP solutions for customer and internal agent experiences. The role involves applying LLM-based methods, rigorous metrics, and ensuring solutions are reliable, secure, and scalable. Responsibilities include full product development lifecycle, advanced analytics, and system development in NLP and machine learning, with a focus on intelligent search, summarization, classification, and next-best-action recommendations.

What you'd actually do

  1. Apply deep natural language processing (NLP) knowledge & experience and critical thinking skills and perform advanced analytics with the goal of solving complex and multi-faceted business problems.
  2. Contribute to the full product development lifecycle, including defining the objective and key product deliverables.
  3. Act as an advanced contributor in system development, computer algorithms, NLP and machine learning.
  4. Contribute to the continuous learning mindset of the organization by bringing in new knowledge, ideas, and perspectives.

Skills

Required

  • PhD degree in Computer Science, Data Science or similar with 10+ years of experience Or Master’s degree in Computer Science, Data Science or similar with 15+ years experience
  • work experience in LLM/NLP, Generative AI, Agentic AI and search
  • Outstanding written and oral communication skills
  • Advanced demonstratable programming skills on more than 1 programming language such as Spark, Python, Scala, Java
  • Can learn quickly programming in another programming language and seamlessly
  • Full understanding of and hands-on programming with data structures, algorithms, operating systems, compilers, databases, and systems

Nice to have

  • Undergraduate and master’s degree in computer science with concentration in NLP or Search experience
  • experience in developing large-scale machine learning solutions based on big data to solve real world problems (e.g. Classification, Regression, or Recommender Systems)

What the JD emphasized

  • LLM-based methods
  • reliable, secure, and scalable
  • cutting-edge Natural Language Processing (NLP) solutions
  • customers and internal agents
  • intelligent search, summarization, classification, and next-best-action recommendations
  • LLM/NLP, Generative AI, Agentic AI and search

Other signals

  • LLM-based methods
  • reliable, secure, and scalable
  • cutting-edge Natural Language Processing (NLP) solutions
  • customers and internal agents
  • intelligent search, summarization, classification, and next-best-action recommendations