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AI Beneficial Ownership Graph Analyst

Jordan Lindqvist

Full-time · Job · New_York

About the role

Shell companies exist to obscure who actually controls an asset. A sanctioned individual owning 24% of a holding company that owns 33% of an operating entity doesn't show up in most compliance screening systems — which is why sophisticated money laundering and sanctions evasion depend on precisely these structures. Understanding beneficial ownership means traversing those graphs accurately and quickly. We build corporate intelligence systems for compliance teams at 42 financial institutions. Our beneficial ownership graph covers 90 million entities across 180 jurisdictions, updated daily from corporate registries, regulatory filings, and proprietary research. The ML challenge is multi-pronged: entity resolution (is 'J. Smith Holdings LLC' the same entity as 'John Smith Holdings LLC'?), ownership percentage inference where structures are partially disclosed, and ultimate beneficial owner identification through chains of arbitrary depth. You'll lead the graph intelligence engineering: designing the models that improve entity resolution accuracy, building ownership inference for partially disclosed structures, and developing the risk scoring that surfaces high-probability compliance risks to human analysts. Your output stops financial crime — that's not marketing language, it's the job.

Responsibilities

  • Design entity resolution models for corporate ownership graph at 90M entity scale
  • Build ownership percentage inference for partially or ambiguously disclosed structures
  • Implement ultimate beneficial owner identification through arbitrary-depth ownership chains
  • Develop compliance risk scoring highlighting structures with sanction exposure
  • Create entity matching evaluation benchmarks with legal entity expert validation
  • Monitor model accuracy against regulatory disclosures and known enforcement cases

Requirements

  • 6+ years in ML engineering with graph analytics or financial crime detection experience
  • Experience with entity resolution, record linkage, or knowledge graph construction
  • Strong Python, SQL, and Spark skills for large-scale graph data processing
  • Understanding of corporate ownership structures and beneficial ownership regulations
  • Familiarity with FINCEN, FATF, and corporate registry data sources

Benefits

  • Fully remote with flexible schedule
  • Comprehensive health benefits
  • $4,000 annual professional development budget
  • Equity participation
  • Work with direct anti-financial-crime impact

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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

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Skills

python pytorch spark sql statistics pandas

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