Machine Learning Intelligent Operations Team - Quant Analytics Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · OH · Consumer & Community Banking

Quant Analytics Senior Associate role focused on optimizing Conversational Insights and Insights for Coaching products using LLM and GenAI models. Responsibilities include end-to-end data analysis, transforming complex datasets, automating reporting, performing business driver analysis, integrating business assumptions, validating hypotheses, identifying production flaws, creating actionable insights, investigating and resolving issues in data collection, model development, and production phases, developing SQL and Python codebases, and documenting data dictionaries and model outputs. The role requires strong programming skills in SQL and Python, experience with AWS, Spark/EMR, ChatGPT, Confluence, Snowflake, multivariate statistics, regression analysis, machine learning, NLP, and LLMs. Experience in consumer banking units is preferred.

What you'd actually do

  1. Conduct end-to-end data analysis to enhance LLM and GenAI model performance across both AWS and on-premises environments.
  2. 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.
  3. Collaborate with cross-functional teams to integrate business assumptions with production data, validate hypotheses, and identify production flaws.
  4. Create and present clear, actionable insights to peers, executive leadership, and business partners.
  5. Proactively investigate and resolve issues in data collection, model development, and production phases.

Skills

Required

  • 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).

Nice to have

  • 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.

What the JD emphasized

  • end-to-end data analysis
  • LLM and GenAI model performance
  • customer self-service
  • causal inference and machine learning techniques
  • production data
  • production flaws
  • production phases

Other signals

  • LLM and GenAI model performance
  • customer self-service
  • causal inference and machine learning techniques