Emmanuel R.

Key Skills

Software

Labelbox

Other

CloudFactory

Micro1

Mercor

Telus

Remotasks

OneForma

Top Subject Matter

Obstetric emergencies

maternal-child health

and clinical decision support for healthcare AI safety

Top Data Types

Text

Video

Audio

Document

Top Task Types

Text Generation

Data Collection

Transcription

Evaluation/Rating

Text Summarization

Polygon

Question Answering

Red Teaming

Freelancer Overview

Senior Clinical & Education Consultant (AI Collaboration) — Global Maternal Health Initiative / AI Lab Partnership. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox and Other. Education includes Doctor of Philosophy, Stanford University (2021) and Master of Science in Nursing, Harvard University (2016). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Evaluation, Rating, and Red Teaming.

Labeling Experience

Midwifery Educator & Instructional Designer — Johns Hopkins University (Center for Health Professions Education)

OtherTextText GenerationRed Teaming

Developed simulation-based education materials for obstetric emergencies that supported downstream AI training and evaluation of clinical competency. Led faculty development and standardized assessment tools to improve the reliability of how clinical behaviors and responses are measured in learning datasets. Conducted and published research on simulation efficacy and education technology to inform iterative improvements to training content. • Built simulation-based curricula for PPH, eclampsia, and shoulder dystocia • Created standardized evaluation tools for feedback and assessment • Supported competency-based training design that can be used to evaluate AI tutoring outputs • Published research on simulation efficacy and education technology

2023 - 2024

Senior Clinical & Education Consultant (AI Collaboration) — Global Maternal Health Initiative / AI Lab Partnership (Remote)

LabelboxText

Led annotation guideline and evaluation framework development for over 2,500 obstetric and clinical case scenarios used in AI model training. Applied clinical diagnosis and diagnostic interpretation skills to assess AI-generated healthcare recommendations for accuracy and safety. Conducted treatment planning and medical-reasoning evaluations to ensure evidence-based and patient-centered AI outputs. • Defined rubric requirements and evaluation criteria for obstetric emergency and clinical case datasets. • Performed bias detection and error taxonomy work to support safe decision-support behavior. • Assessed pharmacology accuracy, patient risk assessment, and ethical compliance in AI recommendations. • Built multilingual healthcare training datasets for culturally diverse settings across East Africa, South Asia, and North America.

2021 - 2023

Clinical Midwife & Research Fellow — St. Mary’s Teaching Hospital (Nairobi, Kenya)

OtherTextData Collection

Worked as a clinical midwife and research fellow in a low-resource maternal health setting, contributing clinical data and contextual knowledge that can be used to support medical dataset creation and study-based labeling. Supervised nursing and midwifery trainees with an emphasis on bedside teaching and mentorship, aligning observed clinical events with structured educational and research documentation practices. Participated in research studies on maternal outcomes to support evidence-based dataset generation. • Managed labor ward caseload and conducted emergency interventions informing clinical ground truth • Mentored trainees and supported consistent documentation/observation practices • Contributed to clinical research studies on maternal outcomes in low-resource settings • Provided contextual expertise relevant for maternal health data labeling and annotation

2014 - 2017

Education

Work History