Applied AIML Data Scientist Lead Vice President

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 78 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $184k

$133k most similar roles pay here $233k

This listing doesn't post a salary. Most similar roles pay $145,200–$223,337.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Applied AIML Data Scientist Lead Vice President

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As an Applied AIML Data Scientist Lead - Vice President on the Legal Applied AI/ML team in Corporate Technology, you will address complex business problems such as semantic search, question answering, and document analysis. You will develop, test, and evaluate AI/ML applications by applying advanced algorithms to internal and external data sources. Your daily work involves building GenAI and LLM solutions, implementing optimization strategies for fine-tuning generative models, and executing the full model development lifecycle including data wrangling and training. You will utilize Python, SQL, Spark, AWS, TensorFlow, PyTorch, Scikit-learn, and OpenAI APIs to build RAG and Agentic AI frameworks. You must translate technical results into actionable insights for non-technical audiences while leveraging techniques like Chain-of-Thoughts and Transformer architectures to solve specific problems within the legal domain through advanced machine learning.

Skills

GenAI LLM Python Spark AWS SQL TensorFlow PyTorch Scikit-learn OpenAI API RAG Agentic AI Transformer NLP Data Wrangling Time Series Deep Learning Reinforcement Learning Java R

What you'll do

Develop GenAI and LLM solutions to solve complex business problems like semantic search and document analysis.
Implement optimization strategies to fine-tune generative models for specific use cases.
Execute the full model development lifecycle including data wrangling, training, testing, and selection.
Design and implement pipelines using RAG and Agentic AI frameworks.
Translate technical results into actionable business insights for non-technical audiences.
Stay current on AI research to implement cutting-edge techniques and integrate external APIs.
Integrate user feedback to develop agentic refinement and self-improving AI applications.

What we're looking for

PhD in Computer Science or a related quantitative discipline with 2+ years of relevant experience.
MS/BS in Computer Science or a related field with 4+ years of relevant experience.
Practical expertise with LLM projects and other supervised and unsupervised techniques.
Proven track record of deploying AI/ML applications in a production environment.
Proficiency in Python and SQL, with experience in other languages like R or Java.
Experience working with large, complex datasets and using frameworks like TensorFlow, PyTorch, Scikit-learn, and OpenAI API.
Solid understanding of statistics, machine learning fundamentals, and generative model architectures like Transformers.
Ability to communicate technical AI/GenAI results and insights to non-technical audiences.

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