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Casimir Ogbolu

IT Risk & Data Analyst — Data Labeling & Annotation

United Kingdom flagLiverpool, United Kingdom
$150.00/hrExpertOther

Key Skills

Software

Other

Top Subject Matter

IT Audit
Healthcare Data & Medical Documentation
Financial Controls

Top Data Types

DocumentDocument
TextText
ImageImage

Top Task Types

ClassificationClassification
Data CollectionData Collection
Evaluation/RatingEvaluation/Rating

Freelancer Overview

IT Risk & Data Analyst — Data Labeling & Annotation. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Streamlit. Education includes Bachelor of Science, University of Liverpool (2023). AI-training focus includes data types such as Document, Medical, and DICOM and labeling workflows including Classification.

ExpertEnglish

Labeling Experience

IT Risk & Data Analyst — Data Labeling & Annotation

DocumentClassification
Responsible for labeling and validating data samples during IT controls testing for audit model training. Tagged control gaps and anomalies to support audit classification models, ensuring accurate labeling for healthcare and finance audit data. Automated data sampling and validation workflows using Python to deliver clean, high-quality labeled datasets. • Annotated and reviewed documentation for risk gaps and anomalies. • Collaborated with cross-domain teams on audit data integrity. • Conducted manual and automated annotation for classification. • Flagged ambiguous samples to improve labeling consistency.

Responsible for labeling and validating data samples during IT controls testing for audit model training. Tagged control gaps and anomalies to support audit classification models, ensuring accurate labeling for healthcare and finance audit data. Automated data sampling and validation workflows using Python to deliver clean, high-quality labeled datasets. • Annotated and reviewed documentation for risk gaps and anomalies. • Collaborated with cross-domain teams on audit data integrity. • Conducted manual and automated annotation for classification. • Flagged ambiguous samples to improve labeling consistency.

2026 - Present

HealthPulse — Patient Risk Annotation Project

DocumentClassification
Labeled and preprocessed over 500,000 patient records for real-time risk prediction model development in HealthPulse. Developed a tiered label taxonomy for patient risk and led annotation validation prior to ML model training. Ensured annotation quality and consistency throughout the dashboard development lifecycle. • Defined and applied risk level labels for prediction. • Automated preprocessing and normalization tasks. • Managed annotation audit cycles for data integrity. • Contributed to live dashboard annotation support.

Labeled and preprocessed over 500,000 patient records for real-time risk prediction model development in HealthPulse. Developed a tiered label taxonomy for patient risk and led annotation validation prior to ML model training. Ensured annotation quality and consistency throughout the dashboard development lifecycle. • Defined and applied risk level labels for prediction. • Automated preprocessing and normalization tasks. • Managed annotation audit cycles for data integrity. • Contributed to live dashboard annotation support.

2023 - 2026

Data Analyst & Systems Administrator — Annotation & Labeling

DocumentClassification
Processed and labeled over two million member records monthly in enterprise ETL pipelines for risk scoring and claims classification. Defined and applied labeling schemas in collaboration with clinical and actuarial teams to train supervised machine learning models. Led compliance-focused data governance and annotation quality reviews to ensure GDPR and NHS standards were met. • Developed label schemas for logistic regression and boosting models. • Performed data quality checks on annotation consistency. • Generated Tableau dashboards for labeling KPIs. • Coordinated annotation workflows for large-scale datasets.

Processed and labeled over two million member records monthly in enterprise ETL pipelines for risk scoring and claims classification. Defined and applied labeling schemas in collaboration with clinical and actuarial teams to train supervised machine learning models. Led compliance-focused data governance and annotation quality reviews to ensure GDPR and NHS standards were met. • Developed label schemas for logistic regression and boosting models. • Performed data quality checks on annotation consistency. • Generated Tableau dashboards for labeling KPIs. • Coordinated annotation workflows for large-scale datasets.

2023 - 2026

Anomaly Detection Framework — Transaction Data Labeling

OtherDocumentClassification
Contributed to the labeling of financial transaction data for anomaly detection in scalable big data pipelines. Used stream processing frameworks to validate ground-truth labels and flag ambiguous edge cases for audit resolution. Focused on improving anomaly detection model precision through accurate annotation. • Applied annotation to millions of transaction records. • Flagged complex/edge-case transactions for review. • Utilized Apache Kafka, Spark, and TensorFlow. • Maintained strict labeling and data quality standards.

Contributed to the labeling of financial transaction data for anomaly detection in scalable big data pipelines. Used stream processing frameworks to validate ground-truth labels and flag ambiguous edge cases for audit resolution. Focused on improving anomaly detection model precision through accurate annotation. • Applied annotation to millions of transaction records. • Flagged complex/edge-case transactions for review. • Utilized Apache Kafka, Spark, and TensorFlow. • Maintained strict labeling and data quality standards.

2023 - 2023

Thesis Project — Multi-Condition Clinical EHR Labeling

Classification
Executed a clinical risk stratification project by labeling EHR records across three disease conditions for supervised machine learning training. Employed balanced sampling and SMOTE to ensure high-quality training labels for Random Forest, XGBoost, and SVM models. Validated consistency and annotation quality to achieve strong F1 performance metrics. • Defined disease-specific risk label taxonomies. • Collaborated with clinicians on schema refinement. • Monitored labeling coverage and distribution. • Delivered reliable training sets for ML research.

Executed a clinical risk stratification project by labeling EHR records across three disease conditions for supervised machine learning training. Employed balanced sampling and SMOTE to ensure high-quality training labels for Random Forest, XGBoost, and SVM models. Validated consistency and annotation quality to achieve strong F1 performance metrics. • Defined disease-specific risk label taxonomies. • Collaborated with clinicians on schema refinement. • Monitored labeling coverage and distribution. • Delivered reliable training sets for ML research.

2022 - 2023

Education

U

University of Liverpool

Bachelor of Science, Computer Science

Bachelor of Science
2019 - 2023

Work History

M

Meridian Risk Advisors

IT Risk & Data Analyst

Liverpool
2026 - Present
C

Capital Bluecross

Data Analyst & Systems Administrator

Liverpool
2023 - 2026