Intraday Liquidity Management, Senior Associate

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Corporate Sector

This role focuses on developing and managing intraday liquidity analytics using AI/ML tools to forecast cash positions and manage limits within JPMorgan Chase's Corporate Treasury. The primary responsibility involves utilizing Python and ML to publish daily forecasts, derive insights from payments data, and maintain a back-testing framework for forecast accuracy. Experience with ML or time-series forecasting in financial workflows is required.

What you'd actually do

  1. Utilize Python and AI/ML tools to publish a daily forecast with commentary on intraday flows across key central bank balances, payment channels and business lines.
  2. Leverage Firmwide payments data to derive analytical insights on intraday cash flow patterns in support of early detection of drivers for end-of-day central bank balance movements.
  3. Develop and maintain back testing framework aimed to improve/enhance forecast accuracy.
  4. Own and maintain governance documents supporting forecast assumptions and details of model(s) employed.
  5. Collaborate with the Product & Technology teams on further enhancements and addition of new intraday analytics and forecasts.

Skills

Required

  • Python
  • SQL
  • Machine Learning
  • Time-series forecasting
  • Feature engineering
  • Model performance monitoring
  • Explainable AI

Nice to have

  • Databricks
  • Notebooks
  • Scalable data preparation
  • Productionizing repeatable analytics

What the JD emphasized

  • Python coding (required)
  • Practical experience applying ML or time-series forecasting to real operational or financial workflows (feature engineering, modeling, performance monitoring, explainable outputs).

Other signals

  • Develop and maintain back testing framework aimed to improve/enhance forecast accuracy.
  • Utilize Python and AI/ML tools to publish a daily forecast with commentary on intraday flows across key central bank balances, payment channels and business lines.
  • Practical experience applying ML or time-series forecasting to real operational or financial workflows (feature engineering, modeling, performance monitoring, explainable outputs).