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AI Peer-to-Peer Payment Fraud Scoring Engineer

William Bergmann

Contract · Job · Chicago

About the role

Our clients include Fortune 500 companies in financial services, and one of them just flagged a spike in peer-to-peer payment fraud that their current scoring model doesn't catch until after the money has already moved. This is a four-month contract to build a real-time scoring model tuned specifically for peer-to-peer transfer patterns, which behave differently from card-present or merchant transactions. Reliability and auditability are non-negotiable, so every scoring decision needs to be explainable to a fraud analyst reviewing the case afterward.

Responsibilities

  • Build a real-time fraud scoring model for peer-to-peer transfers
  • Ensure scoring decisions are explainable to fraud analysts
  • Deliver documentation and handoff for the internal fraud team

Requirements

  • 3+ years building fraud or risk scoring models in production
  • Strong scikit-learn and snowflake experience
  • Available for a four-month contract engagement

Benefits

  • Fully remote contract position
  • Competitive contract rate
  • Possibility of extension based on project outcomes

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Categories

data science & analytics Automation & AI Workflows

Skills

python sql scikit-learn statistics snowflake

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Job Type

Contract

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

data science & analytics Automation & AI Workflows

Skills

python sql scikit-learn statistics snowflake

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