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Racheal Onocheta

Racheal Onocheta

Data Scientist in Contract Review, Compliance, and Legal Research

Nigeria flagAbuja, Nigeria
$20.00/hrIntermediateOtherInternal Proprietary Tooling

Key Skills

Software

Other
Internal/Proprietary Tooling

Top Subject Matter

Legal Services & Contract Review
Regulatory Compliance & Risk Analysis
Scientific Data Management & Research Support

Top Data Types

Geospatial Tiled ImageryGeospatial Tiled Imagery
Computer Code ProgrammingComputer Code Programming
DocumentDocument

Top Task Types

Bounding BoxBounding Box
Data CollectionData Collection
Computer Programming/CodingComputer Programming/Coding
ClassificationClassification
SegmentationSegmentation
PolygonPolygon
Point/Key PointPoint/Key Point

Freelancer Overview

Data Scientist in Contract Review, Compliance, and Legal Research. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Bachelor of Agricultural Technology, Federal University of Technology Minna (2017) and Ordinary National Diploma, Federal College of Freshwater Fisheries Technology (2012).

IntermediateEnglish

Labeling Experience

Code Annotation for Machine Learning Model Development

Computer Code ProgrammingComputer Programming Coding
Annotated and structured code datasets for machine learning tasks, including classification and regression projects. Reviewed, labeled, and organized code snippets for readability, functionality, and model training purposes. Worked with Python-based scripts involving data preprocessing, feature engineering, and model development. Ensured quality through code validation, consistency checks, and adherence to coding and annotation standards.

Annotated and structured code datasets for machine learning tasks, including classification and regression projects. Reviewed, labeled, and organized code snippets for readability, functionality, and model training purposes. Worked with Python-based scripts involving data preprocessing, feature engineering, and model development. Ensured quality through code validation, consistency checks, and adherence to coding and annotation standards.

2025 - Present

Geospatial Image Classification for Land Use Analysis

Geospatial Tiled ImageryClassification
Performed classification of tiled satellite imagery into land-use categories such as vegetation, water bodies, urban areas, and bare land. Applied consistent labeling standards across datasets to support supervised machine learning models. Worked with large image sets, ensuring accuracy through validation checks, label consistency, and adherence to annotation guidelines.

Performed classification of tiled satellite imagery into land-use categories such as vegetation, water bodies, urban areas, and bare land. Applied consistent labeling standards across datasets to support supervised machine learning models. Worked with large image sets, ensuring accuracy through validation checks, label consistency, and adherence to annotation guidelines.

2026 - 2026

Geospatial Data Annotation for Satellite Imagery

Geospatial Tiled ImageryBounding Box
Annotated tiled satellite imagery for land-use classification, including vegetation, water bodies, and built-up areas. Applied polygon and bounding box annotations with high spatial accuracy. Ensured data quality through validation, consistency checks, and adherence to labeling guidelines for machine learning use.

Annotated tiled satellite imagery for land-use classification, including vegetation, water bodies, and built-up areas. Applied polygon and bounding box annotations with high spatial accuracy. Ensured data quality through validation, consistency checks, and adherence to labeling guidelines for machine learning use.

2026 - 2026

Geospatial Data Annotation for Satellite Imagery

Geospatial Tiled ImageryData Collection
Annotated tiled satellite imagery for land-use classification, including vegetation, water bodies, and built-up areas. Applied polygon and bounding box annotations with high spatial accuracy. Ensured data quality through validation, consistency checks, and adherence to labeling guidelines for machine learning use.

Annotated tiled satellite imagery for land-use classification, including vegetation, water bodies, and built-up areas. Applied polygon and bounding box annotations with high spatial accuracy. Ensured data quality through validation, consistency checks, and adherence to labeling guidelines for machine learning use.

2026 - 2026

Education

F

Federal University of Technology Minna

Bachelor of Agricultural Technology, Food Science and Nutrition

Bachelor of Agricultural Technology
2013 - 2017
F

Federal College of Freshwater Fisheries Technology

Ordinary National Diploma, Food Technology

Ordinary National Diploma
2010 - 2012

Work History

F

Freelance

Data Scientist

Abuja
2025 - Present
L

Livingston Research

Freelance Researcher

N/A
2023 - 2024