The role
From JPMorgan Chase's own posting.
Join a team that unifies data and analytics talent across Chase to responsibly leverage data to build competitive advantages for our businesses with value and protection for customers.
As a Quant Analytics Senior Associate, within the MLIO Analytics team, you will play a key role in optimizing Conversational Insights and Insights for Coaching products, span the entire problem-solving lifecycle, including: identifying gaps through thorough and inquisitive analyses, conducting root cause analyses using causal inference and machine learning techniques, and identifying opportunities to enable customer self-service across various Chase customer engagement channels.
Job Responsibilities:
Conduct end-to-end data analysis to enhance LLM and GenAI model performance across both AWS and on-premises environments.
Transform complex datasets, automate reporting, and perform business driver analysis to improve operational processes and applications. Utilize advanced methods to identify customer and specialist friction points across multiple interaction channels.
Collaborate with cross-functional teams to integrate business assumptions with production data, validate hypotheses, and identify production flaws.
Create and present clear, actionable insights to peers, executive leadership, and business partners.
Proactively investigate and resolve issues in data collection, model development, and production phases.
Develop SQL and Python codebases, and document data dictionaries and model outputs in a clear and concise manner.
Serve as a member of agile, digital, or scrum teams, providing data and analytics support for new product development.
Required qualifications, capabilities, and skills:
Bachelor’s degree in Economics, Data Analytics, Statistics, or a STEM related field; 6 years of work experience in Analytics or Master’s degree in Economics, Data Analytics, Statistics, or a STEM related field; 2 year of work experience in Analytics
Solid programming skills in SQL.
Hands-on experience with Excel PivotTables, PowerPoint presentations, and data wrangling and visualization tools including Tableau, Alteryx and Python Jupyter Notebook.
Trained in multivariate statistics, regression analysis, Python, SQL and visualization tool including Tableau.
Professional experience with AWS, Spark/EMR, ChatGPT, Confluence, and Snowflake.
Strong understanding of multi-linear regression, logistic regression, clustering, classification techniques including LightGBM and XGBoost, controlled experiments, and causal inference methods (DD, PM, NN).
Experience with Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLM).
Preferred qualifications, capabilities, and skills:
Extensive experience in consumer banking units such as operations, servicing, collections, or marketing.
Proficient in big data ETL processes for both structured and unstructured databases.
Good understanding of IT processes and databases, with the ability to work directly with data owners and custodians, contributing to the development of analytics data hubs.