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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.
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As a Machine Learning Intelligent Operations Team - Quant Analytics Senior Associate, you will join the MLIO Analytics team to optimize Conversational Insights and Insights for Coaching products. You will manage the entire problem-solving lifecycle by identifying gaps through inquisitive analysis, conducting root cause analyses using causal inference and machine learning, and enabling customer self-service across various engagement channels. Your daily work involves performing end-to-end data analysis to enhance LLM and GenAI model performance in AWS and on-premises environments while automating reporting and transforming complex datasets. You will develop SQL and Python codebases, manage data wrangling with Tableau and Alteryx, and utilize Spark/EMR and Snowflake. Technical expertise required includes multivariate statistics, regression analysis, clustering, classification techniques like LightGBM and XGBoost, and experience with Natural Language Processing to solve operational friction points.
Conduct end-to-end data analysis to enhance LLM and GenAI model performance across AWS and on-premises environments.
Transform complex datasets and automate reporting to improve operational processes and applications.
Perform business driver analysis to identify customer and specialist friction points across multiple interaction channels.
Use causal inference and machine learning techniques to conduct root cause analyses.
Develop SQL and Python codebases while documenting data dictionaries and model outputs.
Investigate and resolve issues in data collection, model development, and production phases.
Present clear, actionable insights to peers, executive leadership, and business partners.
What we're looking for
Bachelor's degree in a STEM field or Economics with 6 years of experience, or a Master's degree in a STEM field or Economics with 2 years of experience.
Solid programming skills in SQL and Python.
Experience with data wrangling and visualization tools including Tableau, Alteryx, and Jupyter Notebooks.
Proficiency in multivariate statistics, regression analysis, clustering, classification techniques (LightGBM, XGBoost), and causal inference methods.
Professional experience with AWS, Spark/EMR, ChatGPT, Confluence, and Snowflake.
Experience with Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLM).
Proficiency in big data ETL processes for structured and unstructured databases (preferred).
Extensive experience in consumer banking units such as operations, servicing, collections, or marketing (preferred).
Python
SQL
Databricks
Apache Spark
Java
JavaScript
Snowflake
ETL
Data Modeling
Tableau
ThoughtSpot
Microsoft Excel
Data Pipelines
Data Quality
Data Architecture
Automation
Reporting