Kusanele M.

Key Skills

Software

Other

Top Subject Matter

Agricultural sensors

operational datasets

AI/BI analytics

Top Data Types

3D Sensor

Text

Document

Top Task Types

Classification

Freelancer Overview

Agrihub | Data Scientist / AI Data Specialist (Data labeling & annotation workflows). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other, TensorFlow, and Docker. Education includes Bachelor of Science (Honours), University of South Africa (UNISA) (2025) and Bachelor of Science, University of South Africa (UNISA) (2022). AI-training focus includes data types such as 3D Sensor, Text, and Document and labeling workflows including Classification, Evaluation, and Rating.

Labeling Experience

Agrihub | Data Scientist / AI Data Specialist (Data labeling & annotation workflows)

Other3D SensorClassification

Designed end-to-end data labeling and annotation workflows for multi-source agricultural time-series and tabular operational data used in AI and BI systems. Applied structured annotation schemas to produce accurate, consistent, model-ready labels and verified data integrity before model training. Produced and maintained labeling documentation, guidelines, and standards for repeatable team execution. • Time-series and tabular schema-based annotation • QA validation to detect annotation errors and inconsistencies • Written labeling methodologies and quality standards • Stakeholder collaboration to convert domain knowledge into labels

2024 - Present

Data Daily | Machine Learning Scientist (Data annotation & QA)

Other3D SensorClassification

Prepared, cleaned, and labeled complex datasets for ML model training, including agricultural yield forecasting with climate variables, soil conditions, and historical crop performance. Built feature labeling schemas and class annotation frameworks aligned to predictive modeling requirements. Conducted rigorous quality assurance checks on training and test sets, including class balance, label consistency, and feature integrity. • Feature labeling and class annotation framework design • Training/test validation for class balance and label consistency • Ground-truth comparison to identify mislabeling patterns • Documentation of labeling decisions and audit trails

2022 - Present

Outreachy | Modeci MDF Data Science Intern (Annotation & validation)

Document

Contributed to annotation and validation of model definition format (MDF) outputs for the Modeci open-source AI project. Reviewed labeled artifacts for correctness and consistency and validated CI/CD pipeline outputs and data artifacts against project quality standards. Documented annotation standards and release procedures to support consistent labeling across an internationally distributed contributor base. • Review and validation of MDF labeled artifacts • CI/CD data artifact QA for format inconsistencies • Documentation of annotation standards and release procedures • Support for reproducible deployment of labeled outputs

2022 - 2023

Explore AI | Data Science Intern (Annotation schemas for ML training)

TextClassification

Designed and applied data labeling schemas for customer behavior datasets to support churn classification, CLV segmentation, and behavioral pattern recognition. Prepared and validated labeled training data for a TensorFlow neural network, including class balancing and annotation quality review. Implemented structured annotation and review processes while adhering to established guidelines and quality standards. • Churn classification and CLV segmentation labeling schemas • Class balancing and annotation quality review for TensorFlow • Edge case handling and guideline-driven consistent labeling • Documentation of labeling decisions for auditability

2022 - 2022

Education

Work History