Derrick C.

Key Skills

Software

Don't disclose

Top Subject Matter

Cross-disciplinary AI evaluation and technical data curation (scientific simulation, software engineering, financial reporting)

Multi-source dataset ingestion

Cleaning Domain Expertise

Top Data Types

Document

Text

Top Task Types

Prompt + Response Writing (SFT)

Tracking

Data Collection

Freelancer Overview

Senior AI Research Fellow & Multi-Domain Curation Lead, CoreSynthetics Systems Lab. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Don't disclose. Education includes Certified Public Accountant, Regulatory Accountancy Board and Doctor of Philosophy, University of Nairobi (2027). AI-training focus includes data types such as Document and labeling workflows including Prompt + Response Writing (SFT), Tracking, and Data Collection.

Labeling Experience

Senior AI Research Fellow & Multi-Domain Curation Lead, CoreSynthetics Systems Lab

DocumentPrompt + Response Writing (SFT)Tracking

Led multi-domain curation to create training and evaluation challenges for foundational AI networks, including prompt structures and constrained answers. Produced citation-supported justifications and reasoning analyses by auditing model responses for errors and anomalies. Ensured deterministic, reproducible evaluation outputs through isolated sandbox environments and well-documented logic. • Authored prompts with mathematical constraints and legacy computing syntax (COBOL/JCL). • Generated labeled evaluation materials emphasizing logical correctness and non-physical anomaly detection. • Performed QA-style checks on citations and reasoning traceability. • Documented data mapping lineages and simulation assumptions for reproducible training/evaluation.

2024 - Present

Full-Stack Software Engineer II & Data Logistics Analyst, Nexus Infrastructure Systems

Don't discloseDocumentData Collection

Engineered modular applications and processing scripts to ingest, clean, and map multi-source datasets used for analytics and model training readiness. Implemented automated exception-tracking and variance analytics routines to monitor data quality and reduce compute overhead. Supported independent code verification reviews to ensure reliable dataset processing and traceability. • Ingested and cleaned multi-source datasets using Python and service logic in TypeScript/Go. • Mapped datasets into structured forms suitable for downstream AI evaluation/training. • Added automated exception tracking for quality assurance of processed records. • Maintained documentation and testing matrices to standardize deployment cycles.

2021 - 2023

Education

Work History