$15.00/hrIntermediateClickworkerAppenGoogle Cloud Vertex AI
Key Skills
Software
Clickworker
Appen
Google Cloud Vertex AI
OneForma
OpenCV AI Kit (OAK)
Remotasks
SuperAnnotate
Axiom AI
Top Subject Matter
Government workflows-Public Administration and Security
Education-E-learning Systems
Machine learning and Data science
Top Data Types
Text
Computer Code Programming
Audio
Top Task Types
Segmentation
RLHF
Fine Tuning
Transcription
Question Answering
Classification
Bounding Box
Freelancer Overview
Data Science Scientist. Brings 12+ years of professional experience across complex professional workflows, research, and quality-focused execution.
Education includes an ongoing Master of Science in Computer Systems, Jomo Kenyatta University of Agriculture and Technology (2022) and Bachelor of Business information Management, Kisii University (2015).
IntermediateSwahiliEnglish
Labeling Experience
Annotator
TextComputer Programming Coding
Focused on creating labeled datasets that enable machine learning models to understand, generate, and evaluate code effectively. The scope included working with different data sources such as raw Python scripts, ipython notebooks, and code snippets, with the goal of capturing intent, structure, and behavior. Also included semantic interpretation—identifying what a function does, how data flows through it, and how different components interact. This often involved enriching code with type hints, docstrings, and structured representations, while also recognizing design patterns such as object-oriented constructs, decorators, and functional abstractions.
Data labeling tasks involved structural tagging, type inference, bug detection, and code quality assessment. Annotators are expected to identify logical errors, edge cases, and inefficiencies, as well as suggest improvements aligned with best practices in python PEP. Quality assurance is critical and is maintained through consistent application of annotation guidelines, technical validation of logic and types, and thorough edge case analysis. Additional checks include inter-annotator agreement to ensure consistency across contributors, schema validation for completeness, and the use of automated tools such as linters and static type checkers. Emphasis is placed on producing annotations that reflect real-world engineering judgment, ensuring the dataset is both accurate and generalizable for downstream AI applications.
Focused on creating labeled datasets that enable machine learning models to understand, generate, and evaluate code effectively. The scope included working with different data sources such as raw Python scripts, ipython notebooks, and code snippets, with the goal of capturing intent, structure, and behavior. Also included semantic interpretation—identifying what a function does, how data flows through it, and how different components interact. This often involved enriching code with type hints, docstrings, and structured representations, while also recognizing design patterns such as object-oriented constructs, decorators, and functional abstractions.
Data labeling tasks involved structural tagging, type inference, bug detection, and code quality assessment. Annotators are expected to identify logical errors, edge cases, and inefficiencies, as well as suggest improvements aligned with best practices in python PEP. Quality assurance is critical and is maintained through consistent application of annotation guidelines, technical validation of logic and types, and thorough edge case analysis. Additional checks include inter-annotator agreement to ensure consistency across contributors, schema validation for completeness, and the use of automated tools such as linters and static type checkers. Emphasis is placed on producing annotations that reflect real-world engineering judgment, ensuring the dataset is both accurate and generalizable for downstream AI applications.
2020 - 2023
Education
K
Kenya School of Government
Diploma, Public Administration
Diploma
2025 - 2026
K
Kisii University
Bachelor of Science, Business Information Management
