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AI Data Sensitivity Classification Developer

Harper Adeyemi

Full-time · Job · New_York

About the role

Most organizations don't know what sensitive data they have or where it is. Healthcare organizations with HIPAA obligations, financial institutions with PCI scope, and companies with GDPR personal data requirements all need to answer 'which tables contain which sensitivity classes of data' — and the answer changes constantly as new datasets are ingested and schemas evolve. Our data governance platform serves 65 enterprise clients managing compliance across complex data environments. Data sensitivity classification — automatically identifying and tagging columns containing PII, PHI, PCI data, or business-confidential information — is the foundational capability that everything else depends on. Our current classifier uses pattern matching and is embarrassingly wrong on anything beyond obvious patterns like SSNs and email addresses. You'll rebuild the sensitivity classification system with ML: training models that understand semantic column meaning from names, sample values, and table context, handling the long tail of sensitive data that looks nothing like a credit card number, and building the continuous classification pipeline that rescans as new tables arrive. The output feeds access control, masking policies, and retention management — getting it right has real compliance consequences.

Responsibilities

  • Rebuild data sensitivity classifier using ML models trained on column names, sample values, and table context
  • Design training data collection strategy from client-consented labeled examples across sensitivity categories
  • Implement continuous classification pipeline rescanning new tables as they arrive in data catalogs
  • Build confidence scoring routing low-confidence classifications for human review
  • Create classification audit trail supporting compliance documentation requirements
  • Evaluate classifier accuracy against compliance team manual review benchmarks

Requirements

  • 3+ years in data engineering, data governance, or ML for structured data
  • Strong Python, SQL, and Spark skills with data catalog or metadata management experience
  • Understanding of PII, PHI, PCI, and GDPR data sensitivity categories
  • Experience building classification systems for structured or semi-structured data
  • Familiarity with enterprise data compliance frameworks and requirements

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $3,000 annual professional development budget
  • 401(k) with match
  • Equity options

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Categories

MLOps & AI Infrastructure data science & analytics

Skills

python sql spark airflow aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure data science & analytics

Skills

python sql spark airflow aws docker

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