Abang El-Shaddai

Key Skills

Software

Label Studio

Appen

Toloka

Top Subject Matter

Fintech Domain Expertise

Kyc/aml Domain Expertise

event tech

Top Data Types

Document

Text

Image

Top Task Types

Classification

Entity (NER) Classification

Freelancer Overview

AI Data & Systems Lead (Founder/CTO), Aerave. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Label Studio. Education includes Bachelor of Science, Middlesex University (2018) and Advanced Diploma, Aptech (2017). AI-training focus includes data types such as Document and Text and labeling workflows including Classification and Entity (NER) Classification.

Labeling Experience

AI Data & Systems Lead (Founder/CTO), Aerave

DocumentClassification

Managed and structured large transactional and user-event datasets to power fraud-detection and recommendation machine learning models. Defined and enforced QA protocols for KYC document image labeling to achieve regulatory compliance and high model accuracy. Designed annotation schemas for QR event check-in and curated labeled data from real-time ticket scans for analytics and crowd management. • Oversaw quality review and annotation consistency within an Agile framework. • Created structured training data powering millions in transactional model throughput. • Ensured precise entity and transaction classification for effective downstream ML. • Provided mentorship on data workflow and best practices to distributed annotation teams.

Managed and structured large transactional and user-event datasets to power fraud-detection and recommendation machine learning models. Defined and enforced QA protocols for KYC document image labeling to achieve regulatory compliance and high model accuracy. Designed annotation schemas for QR event check-in and curated labeled data from real-time ticket scans for analytics and crowd management. • Oversaw quality review and annotation consistency within an Agile framework. • Created structured training data powering millions in transactional model throughput. • Ensured precise entity and transaction classification for effective downstream ML. • Provided mentorship on data workflow and best practices to distributed annotation teams.

2022 - Present

Freelance Data Contributor, YellowCard (project)

AppenDocumentClassification

Reviewed and labeled identity verification and 2FA challenge documents to facilitate compliance model development for crypto exchange. Annotated multi-channel payment gateway response data to aid transaction classification and anomaly detection tasks. Ensured training data consistency and high-quality annotated sets for model adaptability across multiple markets. • Supported compliance and 2FA challenge record annotation. • Labeled payment data for diverse transaction routes including mobile and card. • Maintained annotation standards across African regulatory jurisdictions. • Promoted best practices for cross-market training data quality.

Reviewed and labeled identity verification and 2FA challenge documents to facilitate compliance model development for crypto exchange. Annotated multi-channel payment gateway response data to aid transaction classification and anomaly detection tasks. Ensured training data consistency and high-quality annotated sets for model adaptability across multiple markets. • Supported compliance and 2FA challenge record annotation. • Labeled payment data for diverse transaction routes including mobile and card. • Maintained annotation standards across African regulatory jurisdictions. • Promoted best practices for cross-market training data quality.

2021 - 2022

AI Data Contributor, Furex (project)

Label StudioTextClassification

Structured and labeled financial transaction records for KYC/AML compliance review in a crypto and giftcard trading ecosystem. Designed annotation schemas for real-time price and risk data, forming the basis of fraud detection and classification ML pipelines. Generated ground-truth annotated datasets supporting anomaly detection and risk scoring for multiple cryptocurrencies. • Implemented schemas for BTC, ETH, USDC/USDT price flows. • Supported KYC protocol adherence and identity data validation. • Created data assets for real-time fraud and risk classification. • Curated annotated records for model explainability and reproducibility.

Structured and labeled financial transaction records for KYC/AML compliance review in a crypto and giftcard trading ecosystem. Designed annotation schemas for real-time price and risk data, forming the basis of fraud detection and classification ML pipelines. Generated ground-truth annotated datasets supporting anomaly detection and risk scoring for multiple cryptocurrencies. • Implemented schemas for BTC, ETH, USDC/USDT price flows. • Supported KYC protocol adherence and identity data validation. • Created data assets for real-time fraud and risk classification. • Curated annotated records for model explainability and reproducibility.

2021 - 2022

Content Data Analyst / Full Stack Developer, NewsWireNGR

Label StudioTextEntity Ner Classification

Labeled, categorized, and tagged over 50,000 monthly news article records for recommendation and NLP-based classification engines. Built and led editorial annotation workflows for entity, sentiment, and topic classification to ensure creation of high-quality training corpora. Implemented Named Entity Recognition (NER) pipelines, improving content discoverability and annotation audit processes by 40%. • Conducted frequent annotation quality audits and resolved team disagreements. • Developed GraphQL APIs to surface annotation and validation metadata for ML. • Focused on classification and tagging of people, organizations, and locations. • Empowered journalists with tools for structured, annotation-driven editorial output.

Labeled, categorized, and tagged over 50,000 monthly news article records for recommendation and NLP-based classification engines. Built and led editorial annotation workflows for entity, sentiment, and topic classification to ensure creation of high-quality training corpora. Implemented Named Entity Recognition (NER) pipelines, improving content discoverability and annotation audit processes by 40%. • Conducted frequent annotation quality audits and resolved team disagreements. • Developed GraphQL APIs to surface annotation and validation metadata for ML. • Focused on classification and tagging of people, organizations, and locations. • Empowered journalists with tools for structured, annotation-driven editorial output.

2020 - 2022

AI Data Contributor, TransIt (project)

TolokaTextClassification

Structured booking and route datasets with semantic labels to drive ML-powered demand forecasting for a transport SaaS platform. Created standards to annotate origin, destination, timing, and service class to ensure model readiness for 5,000+ monthly bookings. Enabled fine-tuned classification of historical and real-time transport data for predictive analytics and business operations. • Tagged bookings for origin-destination and service segmentation. • Designed label schemas tailored to dynamic route data. • Provided annotated corpora to optimize transport capacity and route predictions. • Ensured clean, explainable training data across multiple data pipelines.

Structured booking and route datasets with semantic labels to drive ML-powered demand forecasting for a transport SaaS platform. Created standards to annotate origin, destination, timing, and service class to ensure model readiness for 5,000+ monthly bookings. Enabled fine-tuned classification of historical and real-time transport data for predictive analytics and business operations. • Tagged bookings for origin-destination and service segmentation. • Designed label schemas tailored to dynamic route data. • Provided annotated corpora to optimize transport capacity and route predictions. • Ensured clean, explainable training data across multiple data pipelines.

2020 - 2021

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Education

Work History