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AI Synthetic Identity Fraud Network Analyst

Jordan Lindqvist

Full-time · Job · New_York

About the role

We just closed a partnership with two regional banks that pushed our synthetic identity exposure up overnight, and our existing shell-company and beneficial-ownership models don't catch the specific pattern these rings use. Synthetic identities are built from real and fabricated data blended together, so they pass individual checks while forming detectable clusters once you look at the network. You will design graph-based models that surface those clusters before the accounts season into active fraud. You'll report to our fraud data science lead and work directly with the banks' investigation teams to validate flagged clusters against confirmed case outcomes.

Responsibilities

  • Design graph-based models detecting synthetic identity clusters across account networks
  • Validate flagged clusters against confirmed fraud investigation outcomes
  • Build Spark-based pipelines processing account and transaction graphs at scale
  • Partner directly with bank investigation teams on case feedback loops
  • Continuously tune detection thresholds as fraud rings adapt their patterns

Requirements

  • 6+ years in applied fraud data science, ideally with graph or network analysis
  • Strong Python, SQL, and statistics background
  • Experience with Spark or comparable large-scale data processing
  • Comfortable working directly with bank investigation and compliance teams

Benefits

  • Fully remote position
  • Health, dental, and vision insurance
  • Equity package
  • Direct exposure to real investigation outcomes

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Categories

data science & analytics AI & Machine Learning Engineers

Skills

python statistics sql spark

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Job Type

Full-time

Level

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Language

English

Salary Range

$130k – $160k / year

AI Expertise

data science & analytics AI & Machine Learning Engineers

Skills

python statistics sql spark

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