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Fraud & Risk Analyst

Lyft

Toronto, ONOn Site

Original Job Summary

Overview

At Lyft, our purpose is to serve and connect. The Fraud & Risk Analyst role is central to ensuring trust and safety across the Lyft marketplace. This role is part of the Fraud and Risk Analytics team which works fast-paced, high-energy, and meticulously detects and prevents fraud losses.

Responsibilities

  • Develop and implement fraud detection strategies and real-time fraud rules.
  • Support cross-functional teams in building dashboards and measurement tools.
  • Collaborate with Product Management, Data Science, Engineering, and Operations.
  • Communicate complex information effectively across all organization levels.
  • Enhance ATO detection and mitigation plans and establish effective business cases.
  • Create robust, customer-friendly policies balancing fraud prevention with user experience.

Experience & Skills

Candidates should have 5+ years of experience with expertise in fraud/identity and strong technical skills in SQL, SAS, R, Python or similar. Excellent communication, strong organizational skills, and the ability to work across teams is essential.

Benefits

  • Extended health, dental, life insurance, and disability benefits.
  • Mental health, childcare, and family building benefits.
  • Flexible paid time off and commuter benefits.
  • Hybrid work model with in-office expectations 3 days per week.

Key skills/competency

  • Fraud Prevention
  • Risk Analysis
  • Data Analytics
  • SQL
  • Python
  • Cross-functional Collaboration
  • Communication
  • Dashboarding
  • Problem Solving
  • Customer Experience

How to Get Hired at Lyft

🎯 Tips for Getting Hired

  • Customize Resume: Tailor experiences with fraud prevention skills.
  • Research Lyft: Understand their mission, culture, and recent initiatives.
  • Highlight Technical Skills: Emphasize SQL, Python, and data analysis.
  • Prepare Examples: Show case studies of fraud mitigation projects.
  • Practice Communication: Be ready for executive-level discussions.

📝 Interview Preparation Advice

Technical Preparation

Review SQL query techniques and data extraction.
Brush up on Python and statistical modeling.
Practice using SAS/R for data analytics.
Analyze case studies on fraud prevention methods.

Behavioral Questions

Describe a time you solved a complex problem.
Explain handling cross-functional collaboration challenges.
Discuss managing multiple projects simultaneously.
Share an example of clear, effective communication.