Senior ML & Data Engineer — Data Quality & Sensitive Data Compliance
New
T
TuringEnterprise AI
This is a full-time remote role based in Brazil or Colombia.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- About 4 to 5 years of hands-on machine learning experience
- Required Skills
- PythonSQLMachine Learning
Requirements
- About 4 to 5 years of hands-on machine learning experience, with ML as your primary background.
- Strong Python for ML development and data validation.
- Strong SQL and experience validating data across pipeline stages.
- Experience building and testing NER or other ML-based detection systems.
- Experience building labeled evaluation sets, computing precision and recall, and handling non-determinism.
- Familiarity with sensitive-data categories and relevant standards, including HIPAA Safe Harbor, GDPR/LGPD, PCI DSS, and confidentiality/NDA obligations.
- Understanding of re-identification risk and how details can combine to reveal an organization or individual.
- A skeptical, detail-oriented approach to validating data and detection results.
- Comfort with ambiguity and a fast-moving environment.
Responsibilities
- Assess enterprise data quality across connectors, including topic coherence, domain depth, completeness, and consistency.
- Design and automate pipeline validation suites for schema checks, completeness, drift detection, and reconciliation across raw, processed, and sanitized stages.
- Design, train, and evaluate ML models that detect sensitive entities in text and image-based documents.
- Build replacement pipelines that map the same entity consistently across files in a corpus.
- Build adversarial de-identification test sets covering edge cases, obfuscated identifiers, multilingual entities, OCR noise, and formats designed to evade detectors.
- Measure and report de-identification performance by data class, including precision, recall, leak rates, false-negative audits, and replacement consistency.
- Implement CI/CD regression gates for data quality and sensitive-data checks.
- Run sampling-based human-in-the-loop audits and maintain audit trails as compliance evidence.
- Partner with engineering on root-cause analysis of inconsistencies or leaks and drive fixes to closure.
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