Jermaine M.

Key Skills

Software

Other

Top Subject Matter

AI/ML education (recommendation systems and matrix factorization)

Recommendation systems and prediction model training for e-commerce/live events

Top Data Types

Text

Top Task Types

Fine-tuning

Classification

Entity (NER) Classification

Freelancer Overview

AI Education Teaching Assistant (internal AI training). Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, University of Notre Dame (2020) and Bachelor of Science, University of Arkansas at Little Rock. AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

Teaching Assistant (Data Science Education) - Apple

TextClassificationEntity (NER) Classification

Designed course content on recommendation systems and matrix factorization for Apple’s internal AI education program, teaching machine learning concepts to technical staff. Held office hours and provided instruction on regression, classification, clustering, deep learning, and AI fundamentals, translating complex ML concepts into accessible learning materials. Supported learner understanding through explanations, guidance, and structured educational materials aligned to ML best practices. • Created teaching materials for recommendation systems and matrix factorization. • Delivered instruction across core ML topics including deep learning concepts. • Conducted office hours and provided technical explanations for learners. • Translated complex ML ideas into clear learning resources.

2019 - 2020

AI Education Teaching Assistant (internal AI training)

Provided AI education focused on recommendation systems and matrix factorization, supporting evaluation of model behavior and learning outcomes. Developed course materials and lesson content used for internal AI training. Supported participant understanding through structured instruction and feedback mechanisms. • Recommendation systems and matrix factorization • Regression, classification, and clustering fundamentals • Deep learning concepts and AI fundamentals • Model behavior evaluation via training exercises

2019 - 2020

Data Science Research Intern - eBay

TextClassification

Developed LSTM prediction models for forecasting numerical demand of live events using social Twitter data and internal StubHub data. Built and fine-tuned an XGBoost classification model to predict user conversion from landing page to checkout, improving funnel optimization decisions. Designed a pairwise ranking model using custom XGB objective functions to order users based on performer preference and enhance recommendation personalization. • Modeled event demand using LSTM architectures and social data signals. • Optimized conversion prediction with fine-tuned XGBoost classification. • Implemented pairwise ranking with custom objective functions for recommendations. • Built ML solutions integrating external social features with internal datasets.

2018 - 2018

Data Science Research Intern (ML model training & optimization)

OtherFine-tuning

Developed and improved predictive ML models using neural architectures and gradient-boosted learning methods. Applied fine-tuning and task-specific objective functions to improve demand and user conversion predictions. Built ranking approaches to enhance recommendation ordering based on performer preference. • LSTM forecasting models using social Twitter and internal event data • Fine-tuned XGBoost classification for landing-page-to-checkout conversion • Pairwise ranking model with custom XGB objective functions • Recommendation personalization improvements via ranking optimization

2018

Education

Work History