Ayo K.

Key Skills

Software

Don't disclose

AWS SageMaker

Top Subject Matter

Physics experimental data

Data Science

scientific modeling

Top Data Types

Computer Code Programming

Top Task Types

Data Collection

Function Calling

Freelancer Overview

Graduate Student Researcher (Physics Department), Data modeling and insight on experimental data. Brings 14+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and AWS SageMaker. Education includes Doctor of Philosophy, University of Houston (2018). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Data Collection, Evaluation, and Rating.

Labeling Experience

Lead Data Scientist and Quantitative Researcher, directing AI/quantitative modeling initiatives

AWS SageMakerFunction Calling

Directed data strategy and quantitative modeling for fintech ventures using advanced AI frameworks. Built and standardized ML infrastructure (feature store, model registry, and monitoring) to support consistent model deployment across multiple portfolio companies. Developed bespoke AI agents for automated technical content generation and business process automation. • Led data science and quantitative research for fintech products reaching 100,000+ users. • Architected shared ML infrastructure to cut deployment time by 60% across portfolio companies. • Developed AI agents for automated technical content generation and process automation. • Hired and managed data scientists and introduced a research review process to improve reproducibility.

2021 - Present

Data Scientist, end-to-end ML projects for user engagement and retention

Don't disclose

Developed machine learning models to analyze user behavior data and improve engagement and retention metrics. Built interpretable models using statistical analysis and machine learning techniques to support customer lifecycle management. Used performance-focused modeling to predict inactivity duration and churn risk and to anticipate user/platform migration outcomes. • Built interpretable user-behavior models using statistical and ML techniques. • Developed predictive models for engagement optimization, inactivity duration, and churn risk. • Modeled console generation migration and assessed model performance against expectations. • Initiated a stochastic data model to represent complex user behavior.

2019 - 2021

Graduate Student Researcher (Physics Department), Data modeling and insight on experimental data

Don't discloseData Collection

Worked extensively with experimental and user-behavior datasets, transforming raw, multi-dimensional data into structured inputs for physics-based and data-driven modeling. Focused on collecting, manipulating, and analyzing data with algorithms to distill messy measurements into usable insights for downstream model development. Contributed analysis and interpretation that enabled model research and validation in academic settings. • Collected and manipulated multi-dimensional experimental data using Python numerical libraries. • Applied physics equations and algorithms to model complex condensed matter processes. • Distilled disparate experimental measurements into coherent, research-grade findings. • Mentored new group members and supported research workflows with collaborators.

2013 - 2018

Education

Work History