Quantitative Analytics [multiple Positions Available]

JPMorgan Chase JPMorgan Chase · Banking · Chicago, IL +1 · Consumer & Community Banking

This role focuses on designing and developing digital analytics requirements, collaborating on A/B tests, automating ETL processes, performing data manipulation, and conducting statistical analysis using machine learning and predictive modeling. It also involves developing measurement frameworks for marketing strategies using financial modeling and attribution, and building telemetry solutions. The role requires experience with causal impact analysis, A/B testing, data presentation, dashboard development, SQL, experimentation design, financial modeling, attribution methods, Python for data analysis and ML algorithms, Agile principles, data governance, and ETL pipeline automation.

What you'd actually do

  1. Design and develop digital analytics requirements for digital marketing campaigns and clickstream data.
  2. Collaborate with product managers, designers, and developers to design and deploy AB tests and evaluate performance KPIs.
  3. Develop and maintain dynamic and interactive dashboards and automate ETL processes.
  4. Perform data manipulation, structuring, design flow, and query optimization.
  5. Conduct statistical analysis using machine learning methods and predictive modeling.

Skills

Required

  • causal impact and AB testing
  • translating quantitative information into actionable insights and presenting to senior stakeholders
  • calculating, analyzing, and presenting data with Excel using pivot tables, formulas, data analysis add-ons, and VBA programming
  • developing interactive dashboards with Tableau, Streamlit, or Google Data Studio
  • SQL, including common table expressions, window functions, and Snowflake SQL
  • tracking and analyzing data with Segment, Google Analytics, and Adobe Analytics
  • experimentation design, including defining primary success metrics, using T-test formulas to determine required sizing and minimum detectable lift, tagging tracking events, and pulling data for performance
  • financial modeling and incrementality analysis to develop measurement frameworks
  • designing telemetry and usage tracking solutions for business intelligence tools
  • attribution performance measurement methods including weighted contribution, data driven attribution, and media mix modeling
  • analyzing and manipulating data using Python with libraries including pandas and NumPy
  • using and programming machine learning algorithms with Python packages, including scikit-learn to build classification and regression algorithms and unsupervised clustering and collaborative filtering algorithms
  • applying Agile project management principles to define analytics project requirements
  • implementing data governance practices using JIRA to ensure data integrity, accuracy, and consistency
  • designing and deploying AB tests using Adobe Target Signal.IO or Optimizely
  • performing exploratory data analysis within large enterprise Snowflake databases
  • building and automating ETL pipelines using Alteryx

What the JD emphasized

  • statistical analysis using machine learning methods and predictive modeling
  • financial modeling and multi-touch attribution
  • designing telemetry and usage tracking solutions for business intelligence tools
  • using and programming machine learning algorithms with Python packages, including scikit-learn to build classification and regression algorithms and unsupervised clustering and collaborative filtering algorithms

Other signals

  • statistical analysis
  • machine learning methods
  • predictive modeling
  • financial modeling
  • multi-touch attribution