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.