For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

LoginCreate account
Invite to Job
Mo Odeyemi

Mo Odeyemi

Compliance ,Risk, Governance, Data privacy

UNITED_KINGDOM flag
London, United Kingdom
$50.00/hrExpertOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

DocumentDocument

Top Label Types

Entity Ner Classification
Classification
Question Answering
Text Summarization
Data Collection
Segmentation
Relationship
Diagnosis

Freelancer Overview

I have over ten years’ experience working with complex and sensitive datasets in highly regulated environments, where accuracy, consistency and clear decision-making are critical. Much of my work has involved reviewing and categorising large volumes of information — including marketing communications, KYC files, sanctions alerts, contracts and Data Subject Rights cases — against defined regulatory criteria. I regularly apply structured decision frameworks to classify lawful bases, consent types, personal and special category data, financial crime indicators and contractual obligations. This requires careful judgement, attention to edge cases and the ability to apply consistent standards across datasets, which aligns closely with high-quality AI training data annotation. I have also designed quality assurance frameworks, redaction workflows and governance standards that improve data accuracy and reduce ambiguity. Translating complex regulatory requirements into clear, repeatable guidance has been a core part of my role, ensuring consistency across teams and systems. I am particularly well suited to AI training projects involving text classification, risk labelling, sensitive data detection and compliance-focused model validation, where subject-matter expertise and precise annotation materially improve model reliability.

ExpertEnglishYoruba

Labeling Experience

Third-Party Risk & DPA Clause Classification

OtherDocumentEntity Ner ClassificationClassification
Scope of Project The project involved enterprise-wide annotation and classification of third-party vendor contracts and Data Processing Agreements (DPAs) across the EMEA region to support AI-driven contract review and third-party risk assessment. The dataset comprised large volumes of unstructured legal text (PDFs and Word documents), including GDPR Article 28 clauses, cross-border transfer mechanisms, breach notification terms, sub-processor provisions, retention periods and technical and organisational measures. The scope included clause-level classification, named entity recognition (e.g., jurisdictions, timelines), span annotation to highlight mandatory language, and structured risk scoring aligned to regulatory requirements. Quality standards were critical due to the regulatory context. Clear annotation guidelines were defined, with multi-stage QA review and senior validation to ensure consistency. Inter-annotator agreement measures, calibration sessions and documented edge-case

Scope of Project The project involved enterprise-wide annotation and classification of third-party vendor contracts and Data Processing Agreements (DPAs) across the EMEA region to support AI-driven contract review and third-party risk assessment. The dataset comprised large volumes of unstructured legal text (PDFs and Word documents), including GDPR Article 28 clauses, cross-border transfer mechanisms, breach notification terms, sub-processor provisions, retention periods and technical and organisational measures. The scope included clause-level classification, named entity recognition (e.g., jurisdictions, timelines), span annotation to highlight mandatory language, and structured risk scoring aligned to regulatory requirements. Quality standards were critical due to the regulatory context. Clear annotation guidelines were defined, with multi-stage QA review and senior validation to ensure consistency. Inter-annotator agreement measures, calibration sessions and documented edge-case

2023 - 2024

Financial Crime & KYC Risk Annotation Project

OtherDocumentEntity Ner ClassificationSegmentation
The project involved enterprise-wide annotation and risk classification of Financial Crime and KYC case files across the EMEA region to support AI-driven risk detection and onboarding automation. The dataset included large volumes of unstructured and semi-structured data, such as customer due diligence files, PEP and sanctions alerts, adverse media reports, source of wealth documentation and internal escalation notes. The scope covered risk-level classification (low/medium/high), identification of financial crime indicators, tagging of ownership structures and beneficial ownership links, detection of data gaps or inconsistencies, and sensitivity labelling of personal and special category data. Structured risk scoring aligned to AML, KYC and regulatory standards was applied to ensure consistency. Given the regulatory and reputational exposure, strict quality controls were implemented. Detailed annotation guidelines, multi-stage QA review and senior validation ensured accuracy and defen

The project involved enterprise-wide annotation and risk classification of Financial Crime and KYC case files across the EMEA region to support AI-driven risk detection and onboarding automation. The dataset included large volumes of unstructured and semi-structured data, such as customer due diligence files, PEP and sanctions alerts, adverse media reports, source of wealth documentation and internal escalation notes. The scope covered risk-level classification (low/medium/high), identification of financial crime indicators, tagging of ownership structures and beneficial ownership links, detection of data gaps or inconsistencies, and sensitivity labelling of personal and special category data. Structured risk scoring aligned to AML, KYC and regulatory standards was applied to ensure consistency. Given the regulatory and reputational exposure, strict quality controls were implemented. Detailed annotation guidelines, multi-stage QA review and senior validation ensured accuracy and defen

2012 - 2023

Education

B

British Computer Society

Practitioner Certificate in Data Protection, Data Protection

Practitioner Certificate in Data Protection
2020 - 2020
N

N/A

Master of Business Administration, Management

Master of Business Administration
2020 - 2020

Work History

T

TTAS

Consultant – Payments and Compliance,Data privacy,data governance,data quality

London
2020 - Present
S

Sumitomo Mitsui Banking Corporation

Financial Crime and Privacy Manager

London
2018 - 2019