Jinom A.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Government statistics and statistical dataset reporting

LLM-assisted prototyping and prompt-driven AI workflow

Legal Services & Contract Review

Top Data Types

Document

Text

Top Task Types

Classification

Prompt + Response Writing (SFT)

Freelancer Overview

Data Analyst Intern, Niger State Bureau of Statistics (NSBS) (Data collection, labeling/annotation, QA). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Technology, Federal University of Technology, Minna (2024) and West African Senior School Certificate, Emmanuel International College (2015). AI-training focus includes data types such as Document, Computer Code, and Programming and labeling workflows including Classification and Prompt + Response Writing (SFT

Labeling Experience

Independent Projects — Writer & Vibe Coder (AI-assisted dev, prompt-driven workflows)

OtherPrompt + Response Writing (SFT)

Completed independent AI-assisted development work using “vibe coding,” which involved iterative prototyping with AI tools and prompt-driven workflows. Applied statistical thinking and data intuition to guide project decisions and outputs. Focused on continuous self-development across writing, coding, mathematics, and data disciplines. • Used AI tools to design and prototype software ideas • Iterated on project outputs through prompt-based workflows • Applied statistical reasoning to inform implementations • Built and shipped independent projects while learning continuously

2023 - Present

Data Analyst Intern, Niger State Bureau of Statistics (NSBS) (Data collection, labeling/annotation, QA)

Don't discloseDocumentClassification

Served as a Data Analyst Intern where responsibilities included delivering high-accuracy data labeling and annotation for statistical datasets. Performed data validation and quality assurance to keep labeling error rates below 2%. Produced clean, structured datasets for government reporting and decision-making. • Labeled and annotated statistical dataset records • Conducted QA checks and reduced labeling errors below 2% • Assisted with data collection and structuring for reporting • Collaborated to resolve data inconsistencies and meet deadlines

2022 - 2023

Education

Work History