Quantitative Analytics Associate - Fraud Prevention Optimization Strategy

JPMorgan Chase JPMorgan Chase · Banking · Wilmington, DE +1 · Consumer & Community Banking

Quantitative Analytics Associate focused on fraud prevention optimization within a financial institution. The role involves complex data analysis, strategy development, and leveraging advanced analytics, including large language models, to reduce fraud losses and improve customer experience. Requires strong quantitative skills, Python/SAS/SQL proficiency, and experience delivering recommendations to leadership.

What you'd actually do

  1. Interpret and analyze complex data to formulate problem statement, provide concise conclusions regarding underlying risk dynamics, trends, and opportunities.
  2. Use advanced analytical & mathematical techniques to solve complex business problems.
  3. Manage, develop, communicate, and implement optimal fraud strategies to reduce fraud related losses and improve customer experience across credit card fraud lifecycle.
  4. Identify key risk indicators, develop key metrics, enhance reporting, and identify new areas of analytic focus to constantly challenge current business practices.
  5. Provide key data insights and performance to business partners.

Skills

Required

  • Bachelor’s degree in a quantitative field or 3 years risk management or other quantitative experience
  • Background in Engineering, statistics, mathematics, or another quantitative field
  • Advanced understanding of Python, SAS, and SQL
  • Query large amounts of data and transform into actionable recommendations.
  • Strong analytical and problem-solving abilities
  • Experience delivering recommendations to leadership.
  • Self-starter with ability to execute quickly and effectively.
  • Strong communication and interpersonal skills with ability to interact with individuals across departments/functions and with senior level executives

Nice to have

  • MS degree in a quantitative field or 4 or more years risk management or other quantitative experience.
  • Hands on Knowledge of AWS and Snowflake.
  • Advanced analytical techniques like Machine Learning, Large Language Model Prompting or Natural Language Processing will be an added advantage.

What the JD emphasized

  • reduce cost of fraud
  • reducing losses
  • customer impact
  • large language models

Other signals

  • reducing cost of fraud
  • optimizing business processes and decisioning
  • leveraging complex analytics and new tools like large language models