Applied Ai/ml - Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · Chicago, IL +1 · Commercial & Investment Bank

JPMorgan Chase is seeking an Applied AI/ML Senior Associate to analyze business problems, experiment with state-of-the-art models, and develop machine learning and deep learning solutions. The role involves developing end-to-end ML/AutoML/AutoNLP pipelines, operationalizing ML models for use cases like Document Q&A, Search, Information Retrieval, classification, and personalization. The candidate will build batch and real-time prediction pipelines, collaborate with various teams for production deployment, and work with LLMs and Gen AI solutions.

What you'd actually do

  1. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, personalization, or recommendation systems.
  2. Develop end-to-end ML/AutoML/AutoNLP pipelines and operationalize the end-to-end orchestration of the ML models to support the various use cases like Document Q&A, Search, Information Retrieval, classification, personalization, etc.
  3. Build both batch and real-time model prediction pipelines with existing application and front-end integrations.
  4. Collaborate to develop large-scale data modeling experiments, explain complex concepts to senior leaders and stakeholders.
  5. Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production.

Skills

Required

  • BS or MS or PhD in Computer Science or Data Science or Statistics or Mathematical sciences or Machine Learning
  • Strong background in Mathematics and Statistics
  • 5+ years' experience in applying data science, ML techniques to solve business problems
  • one of the programming languages like Python, Java, C/C++
  • Experience with LLMs and Prompt Engineering techniques
  • 1+ year of experience working with Gen AI solutions / LLMs such as GPT, Claude, Llama etc.
  • Solid background in NLP, Generative AI
  • hands-on experience and solid understanding of Machine Learning and Deep Learning methods
  • familiar with large language models
  • Extensive experience with Machine Learning and Deep Learning toolkits (e.g.: Transformers, Hugging Face, TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks
  • ability 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
  • Experience with building and deploying ML models on AWS esp. using AWS tools like Sagemaker, EC2, Glue, etc.
  • good understanding about the Active Learning, Agent/Multi Agent Learning, Learning from Supervision/Feedback, etc.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
  • Ability to work on tasks and projects through to completion with limited supervision
  • Passion for detail and follow through
  • Excellent communication skills and team player

Nice to have

  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journals
  • Experience with A/B experimentation and data/metric-driven product development
  • Ability to develop and debug production-quality code
  • familiarity with continuous integration models and unit test development

What the JD emphasized

  • 5+ years' experience in applying data science, ML techniques to solve business problems
  • 1+ year of experience working with Gen AI solutions / LLMs
  • Solid background in NLP, Generative AI and hands-on experience and solid understanding of Machine Learning and Deep Learning methods and familiar with large language models

Other signals

  • Develop end-to-end ML/AutoML/AutoNLP pipelines
  • operationalize the end-to-end orchestration of the ML models
  • Build both batch and real-time model prediction pipelines
  • Experience with LLMs and Prompt Engineering techniques
  • Experience with Gen AI solutions / LLMs