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I

Idowu Adeolu

AI Data Analyst / Data Rater

United Kingdom flagLondon, United Kingdom
$18.00/hrIntermediateTelusOtherMindrift

Key Skills

Software

TelusTelus
Other
MindriftMindrift
OneFormaOneForma
SuperAnnotateSuperAnnotate
ClickworkerClickworker
AppenAppen

Top Subject Matter

Web Search/Information Retrieval
Large Language Models/Natural Language Processing
Legal Services & Contract Review

Top Data Types

TextText
DocumentDocument
ImageImage

Top Task Types

Bounding Box
Object Detection
Function Calling
Prompt Response Writing SFT
Entity Ner Classification
Point Key Point
Text Generation
Polygon
Question Answering
Evaluation Rating
Computer Programming Coding
Classification

Freelancer Overview

AI Data Analyst / Data Rater. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Telus and Other. Education includes Master of Science, University of Greenwich (2015) and Bachelor of Science, Lead City University (2012). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

IntermediateEnglish

Labeling Experience

AI Trainer / LLM Evaluator

OtherText
This ongoing role focuses on evaluating and training large language models (LLMs) with an emphasis on prompt/response quality and factual accuracy. Responsibilities include rating LLM outputs using standardized rubrics and spotting common error patterns such as hallucinations and irrelevant responses. The experience also entailed creating training examples and supporting dataset quality through QA spot-checking. • Assessed LLM responses for instruction-following and content clarity based on defined grading criteria. • Generated prompt/response training examples under strict formatting and constraint adherence. • Identified and documented common LLM failure modes, providing targeted feedback for improvements. • Performed QA checks on labeled datasets, identifying and addressing issues like duplicates and inconsistency.

This ongoing role focuses on evaluating and training large language models (LLMs) with an emphasis on prompt/response quality and factual accuracy. Responsibilities include rating LLM outputs using standardized rubrics and spotting common error patterns such as hallucinations and irrelevant responses. The experience also entailed creating training examples and supporting dataset quality through QA spot-checking. • Assessed LLM responses for instruction-following and content clarity based on defined grading criteria. • Generated prompt/response training examples under strict formatting and constraint adherence. • Identified and documented common LLM failure modes, providing targeted feedback for improvements. • Performed QA checks on labeled datasets, identifying and addressing issues like duplicates and inconsistency.

2025 - Present
Telus

AI Data Analyst / Data Rater

TelusText
This role involved annotating and evaluating text data to support machine learning and AI model development. Main tasks included rating search result relevance, classifying query intent, and performing guideline-driven content evaluations. Fact verification and escalation of sensitive issues were also key aspects of this experience. • Rated the relevance of search results and classified query intent for ambiguous and clear cases. • Conducted light web research to inform label decisions and ensure accuracy. • Flagged and escalated YMYL-sensitive and policy-violating content as per project guidelines. • Maintained high accuracy and consistency through calibration feedback and reviewer rationales.

This role involved annotating and evaluating text data to support machine learning and AI model development. Main tasks included rating search result relevance, classifying query intent, and performing guideline-driven content evaluations. Fact verification and escalation of sensitive issues were also key aspects of this experience. • Rated the relevance of search results and classified query intent for ambiguous and clear cases. • Conducted light web research to inform label decisions and ensure accuracy. • Flagged and escalated YMYL-sensitive and policy-violating content as per project guidelines. • Maintained high accuracy and consistency through calibration feedback and reviewer rationales.

2025 - 2025

Education

U

University of Greenwich

Master of Science, Computer Science

Master of Science
2015 - 2015
L

Lead City University

Bachelor of Science, Computer and Information Science

Bachelor of Science
2012 - 2012

Work History

H

Honest Properties

QA and Testing Engineer

London
2024 - Present
D

Deutsche Bank

QA and Testing Engineer

London
2018 - 2023