Prudence Uwakwem

Key Skills

Software

Appen

Clickworker

CVAT

Labelbox

Mindrift

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Bounding Box

Data Collection

Entity Ner Classification

Evaluation Rating

Text Generation

Freelancer Overview

I am a detail-oriented data science graduate student with hands-on experience in AI training data, model evaluation, and predictive analytics. My background includes designing structured datasets for AI systems, conducting thorough data validation, and optimizing model performance using Python, SQL, and data visualization tools like Power BI and Tableau. I have led projects that involved analyzing behavioral and location-based data, building supervised learning models, and improving data quality for better classification and recommendation outcomes. I am passionate about ensuring data accuracy, minimizing bias, and supporting AI systems that reflect real-world patterns across sectors such as location-based services and environmental analytics. My strengths include analytical problem-solving, technical documentation, and collaborative work in agile environments. Data type - audio and text.

Labeling Experience

AI Data Modeler – Outlier

LabelboxTextEntity Ner ClassificationText Generation

As an AI Data Modeler at Outlier, I evaluated and refined AI-generated outputs to enhance model accuracy and logical consistency. I focused on identifying weaknesses in AI model reasoning and aligning outputs with real-world patterns. I designed structured prompts and applied statistical reasoning to support robust model evaluation and improvement. • Conducted detailed evaluation and rating of AI-generated textual content. • Proposed and executed structured improvements to address identified reasoning weaknesses. • Designed model stress tests for edge cases through strategic prompt crafting. • Ensured statistical rigor and minimized model bias in data evaluation.

As an AI Data Modeler at Outlier, I evaluated and refined AI-generated outputs to enhance model accuracy and logical consistency. I focused on identifying weaknesses in AI model reasoning and aligning outputs with real-world patterns. I designed structured prompts and applied statistical reasoning to support robust model evaluation and improvement. • Conducted detailed evaluation and rating of AI-generated textual content. • Proposed and executed structured improvements to address identified reasoning weaknesses. • Designed model stress tests for edge cases through strategic prompt crafting. • Ensured statistical rigor and minimized model bias in data evaluation.

2024 - 2025

Education

Work History