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AI Shell Company Network Detector

Jordan Lindqvist

Full-time · Job · New_York

About the role

Shell company networks are the infrastructure of financial crime. A sanctioned individual doesn't hold assets directly — they control them through a chain of nominee-owned holding companies across multiple jurisdictions, each layer adding opacity. Detecting these networks requires connecting the dots across corporate registries, beneficial ownership disclosures, legal filings, and proprietary intelligence sources to reconstruct the actual control structure beneath the nominal ownership. Our beneficial ownership platform covers 90 million entities across 180 jurisdictions. Shell company network detection is our most forensically significant capability: moving from 'we know who the registered owners are' to 'we can identify the probable ultimate beneficial owner of a complex multi-layer structure with high confidence.' This is the analysis that supports OFAC sanctions enforcement, FCPA investigations, and anti-money laundering programs at our 42 financial institution clients. This is a principal-level research role at the intersection of graph ML and financial crime intelligence. You'll design the network detection methodology, build the models, validate findings against known enforcement cases, and present analytical conclusions to client compliance officers and government agency stakeholders. The role is hybrid — 3 days per week in NYC for client meetings and classified briefings.

Responsibilities

  • Design shell company network detection methodology combining graph structure analysis and entity intelligence signals
  • Build ownership chain reconstruction models identifying ultimate beneficial owner through multi-layer corporate structures
  • Implement typology detection identifying characteristic patterns of specific financial crime network architectures
  • Develop confidence scoring and evidence provenance for findings presented to compliance teams and regulators
  • Validate detection accuracy against confirmed enforcement cases and sanctions designation networks
  • Present analytical conclusions to compliance officers at financial institution clients and government agency contacts

Requirements

  • 8+ years in ML engineering with financial crime investigation, sanctions compliance, or intelligence analysis experience
  • Deep expertise in graph-based entity network analysis at 90M+ entity scale
  • Strong Python and Spark skills with corporate data, ownership registries, and legal filing data familiarity
  • Understanding of shell company typologies, sanctions evasion structures, and FINCEN beneficial ownership regulations
  • Track record of analytical work that has contributed to compliance or enforcement outcomes

Benefits

  • Hybrid: 3 days NYC, 2 days remote
  • Top-of-market total compensation for financial crime ML expertise
  • Comprehensive health benefits
  • $5,000 annual professional development budget
  • Significant equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql statistics

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Language

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Salary Range

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AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql statistics pandas

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