Strategic Analytics - Associate

JPMorgan Chase JPMorgan Chase · Banking · Plano, TX +1 · Consumer & Community Banking

This role focuses on developing and implementing data-driven strategies and machine learning models to prevent digital fraud in fintech. The associate will analyze large datasets, identify trends, reduce losses, and enhance customer experience by collaborating with cross-functional teams and presenting insights to senior leadership.

What you'd actually do

  1. Analyze large and complex datasets to uncover trends and behaviors in digital fraud activity.
  2. Develop and implement creative, data-driven strategies and business rules to reduce fraud losses.
  3. Monitor fraud trends and proactively identify emerging risks and vulnerabilities.
  4. Design and automate processes to improve the efficiency and effectiveness of fraud alert systems.
  5. Collaborate with technical and business partners to develop and implement new fraud-fighting products using agile methodologies.

Skills

Required

  • Python
  • SAS
  • R
  • SQL
  • Excel
  • PowerPoint
  • Data Analysis
  • Experiment Design
  • Communication
  • Critical Thinking
  • Teamwork
  • Agile Methodologies

Nice to have

  • Digital Payments
  • Fraud Prevention
  • Financial Services
  • Machine Learning
  • P&L Management
  • Product Development
  • Customer Experience

What the JD emphasized

  • Minimum 2 years of professional experience in analytics, risk management, or data science.
  • Proven ability to analyze and interpret large datasets and translate findings into actionable business strategies.
  • Proficiency in analytical programming languages such as Python, SAS, R, or SQL.
  • Advanced skills in Excel and PowerPoint for data analysis and presentation.
  • Strong communication skills with the ability to convey complex data and concepts clearly.
  • Demonstrated experience in designing and analyzing experiments.
  • Ability to develop concise presentations with sound business conclusions.
  • Strong critical thinking and intellectual curiosity.
  • Experience working collaboratively within teams and across functions.
  • Ability to manage diverse tasks and respond to rapidly changing priorities.
  • Familiarity with machine learning models and their application in fraud detection.

Other signals

  • Develop and implement creative, data-driven strategies and business rules to reduce fraud losses.
  • Monitor fraud trends and proactively identify emerging risks and vulnerabilities.
  • Collaborate with technical and business partners to develop and implement new fraud-fighting products using agile methodologies.