Document Analyst

Odixcity Consulting; España; Presencial; EUR 42.000 - 70.000; Jornada completa

Descripción de la vacante

Odixcity Consulting is seeking a Document Analyst to review and verify documents, ensuring accuracy, authenticity, and regulatory compliance across global data sources.

The role requires analyzing various document types, identifying fraud indicators, and collaborating with ML teams to improve annotation guidelines and data quality. Remote work is supported worldwide, with emphasis on maintaining high data integrity.

Formación

• Minimum of 3 years in Document Analysis or related fields.
• Deep knowledge of identity, financial and legal documents.
• Experience identifying sophisticated document fraud across jurisdictions.

Responsabilidades

• Analyze and validate data from documents to ensure accuracy and completeness.
• Identify document fraud, including forged IDs and deepfake attempts.
• Collaborate with Policy and Product to refine annotation guidelines for global documents.
• Provide feedback to ML engineers on model performance and data quality.
• Quality-check annotations and ensure regulatory compliance by analysts and systems.
• Suggest workflow and tooling improvements to streamline reviews.

Conocimientos

Document analysis

KYC/AML

Data extraction

OCR / annotation platforms

Herramientas

OCR software

Annotation platforms

Descripción del empleo

Job Title: Document Analyst

Location: Remote (Worldwide)

Job Summary: The Document Analyst is responsible for reviewing, verifying, and analyzing documents to ensure accuracy, authenticity, completeness, and compliance with organizational policies and regulatory standards. This role supports operational integrity by identifying discrepancies, validating information, and assessing documentation for potential risk, fraud, or procedural gaps.

Responsibilities

• Analyze and validate data extracted from a wide variety of documents, including government-issued IDs, proof of address, financial statements, legal contracts, and medical forms. Ensure extracted fields (name, date, document number, etc.) are accurate and complete.
• Identify sophisticated document fraud, including photoshopped IDs, counterfeit documents, and deepfake verification attempts. Use forensic analysis techniques to spot anomalies in security features (holograms, watermarks, microprint).
• Handle complex edge cases where documents are damaged, non-standard, or from unfamiliar jurisdictions. Use research skills and domain knowledge to make accurate determinations on document validity and data interpretation.
• Collaborate with Policy and Product teams to create, test, and refine annotation guidelines account for global document diversity and emerging fraud vectors.
• Provide structured feedback to machine learning engineers and data scientists on model performance. Identify systematic errors in OCR or data extraction and contribute to “golden datasets” used for model training and evaluation.
• Perform quality checks on document annotations and verifications completed by junior analysts or automated systems. Maintain high inter-annotator agreement (IAA) and ensure compliance with regulatory requirements.
• Ensure document processing workflows comply with global regulations, including KYC (Know Your Customer), AML (Anti-Money Laundering), GDPR, and eIDAS (Electronic Identification and Trust Services).
• Identify opportunities to streamline document review workflows, improve tooling, and reduce manual touchpoints without compromising accuracy or security.

Requirements

• Minimum of 3 years of experience in Document Analysis, Identity verification, KYC/AML Operations, or Data Extraction within fintech, legal tech, trust & safety, or AI training sectors.
• Deep knowledge of global identity documents (passports, driver’s licenses, national IDs), financial documents (bank statements, pay stubs), and/or legal documents (contracts, incorporation papers).
• Proven ability to identify sophisticated document fraud, including manipulation techniques, forgery indicators, and biometric discrepancies.
• Experience with document verification tools, OCR software, or annotation platforms. Familiarity with data formats such as JSON, XML, or CSV for structured data output.
• Strong understanding of KYC/AML frameworks, data privacy regulations (GDPR, CCPA) and industry standards for identity verification.