Analyst Ii, Full Stack

Affirm · Fintech · United States · Remote · Core Analytics

This role focuses on developing and optimizing fraud decisioning strategies within a fintech company. It involves extensive data analytics, collaborating with cross-functional teams (Product, Engineering, Operations, Finance), and developing new fraud features. A key responsibility is creating scalable frameworks for proprietary fraud machine learning models and evaluating data sources to mitigate fraud risk. The role also involves partnering with the Machine Learning team on fraud and identity verification strategies and owning the end-to-end analytics workflow, including defining metrics and creating dashboards.

What you'd actually do

  1. Proactively explore data and identify opportunities to help accelerate product development or improve existing products in Trust and Safety, and drive the analytical work of online experiment to optimise the existing strategies to achieve better conversion rate.
  2. Translate analysis and trends into recommendations for business logic to improve identity and fraud conversion rates or fraud rates
  3. Develop scalable frameworks to manage tiered cutoffs for proprietary fraud machine learning models
  4. Evaluate innovative data sources to solve fraud risk, and partner with the data engineering team to develop and maintain the data pipelines for our core datasets for analytical purposes.
  5. Own end-to-end analytics workflow, including defining success and performance metrics, socialising them across the organisation, and creating actionable dashboards and reports
  6. Partner with Machine Learning on building fraud and identity verification strategies for new product, market, and user segments
  7. Partner closely with Product and Engineering to identify highest impact points in the funnel to drive user acquisition and retention

Skills

Required

  • SQL
  • Python
  • customer risk management at a payments or financial company
  • fundamentals of payment processing
  • understanding of industry risk trends
  • risk strategy development

Nice to have

  • Bachelor’s or Master’s degree degree in a quantitatively rigorous discipline like engineering, statistics, math, or economics

What the JD emphasized

  • proprietary fraud machine learning models
  • partner with Machine Learning

Other signals

  • fraud machine learning models
  • partner with Machine Learning
  • develop scalable frameworks to manage tiered cutoffs for proprietary fraud machine learning models