Philemon M.

Key Skills

Software

Telus

Appen

Scale AI

Mindrift

Toloka

Top Subject Matter

AI dataset labeling and rubric-based quality evaluation for model-facing content

NLP annotation for classification and named-entity recognition datasets

Annotation QA and rubric-based review for NLP training data

Top Data Types

Image

Video

Text

Audio

Document

Top Task Types

Entity (NER) Classification

Transcription

Classification

Bounding Box

Segmentation

Freelancer Overview

Data Labeling Specialist | TELUS International AI. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Telus, Appen, and Other. Education includes Bachelor of Science, Moi University (2022) and KCSE Certificate, Kiamaina High School (2018). AI-training focus includes data types such as Image, Video, and Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Data Labeling Specialist | TELUS International AI

TelusImage

Reviewed and annotated AI training datasets across image, video, text, and multimodal workflows. Applied strict rubric-based criteria to evaluate accuracy, consistency, contextual relevance, and task adherence. Documented edge cases with concise rationale to support consistent reviewer and QA decisions. • Validate labels, classifications, and attributes against project instructions • Perform pre-submit quality checks against gold-standard expectations • Collaborate with QA leads on guideline updates and batch priorities • Sustain accuracy and production targets while auditing ambiguous examples

2024 - Present

NLP Data Annotator | Appen

AppenTextEntity (NER) Classification

Labeled large English-language text datasets for intent, sentiment, topic classification, and named-entity recognition. Evaluated language samples for meaning, context, tone, and classification accuracy to ensure each annotation matched the intended user request and evidence. Built a decision-tree approach mapped to rubric definitions to handle ambiguous prompts, aliases, abbreviations, and partial mentions. • Apply strict span boundaries and normalization rules • Compare against adjudicated outputs and correct inconsistencies • Log recurring ambiguity patterns with clarification notes • Reduce disagreement rates and accelerate review cycles

2023 - 2024

Speech Data Annotator | TransPerfect DataForce

OtherAudioTranscription

Transcribed and time-aligned English audio clips while preserving verbatim speech, punctuation, speaker turns, and required tags. Reviewed transcripts for omissions, repeated words, unclear segments, grammar consistency, and punctuation accuracy. Corrected segmentation drift by replaying boundary regions and aligning timestamps with duration and quality requirements. • Preserve numbers, names, and non-speech event tags per guidelines • Apply structured QA passes for accuracy and completeness • Ensure final transcripts were readable and faithful to source audio • Handle domain-specific vocabulary according to project requirements

2023 - 2023

Annotation QA Reviewer | iMerit

OtherText

Audited labeled datasets against gold-standard answers and QA rubrics. Identified errors in classification, boundary selection, missing information, attribute misuse, and inconsistent reasoning. Rated and reviewed work using objective criteria to strengthen quality control across large-scale AI training workflows. • Provide precise, actionable feedback with examples of correct vs incorrect • Track defects by category including systematic guideline misinterpretations • Support model integrity by assessing severity of issues • Ensure dataset reliability through analytical review

2023 - 2023

Document & OCR Labeler | Scale AI

Scale AIDocumentClassification

Annotated document images for OCR and information extraction tasks. Tagged tables, headers, footers, key-value fields, multi-line entries, and structured layouts according to requirements. Reviewed extracted text for formatting accuracy, character correctness, field alignment, and faithful representation of source documents. • Escalate ambiguous characters and formatting issues with evidence • Maintain high accuracy, structure, and consistency for training • Support document understanding model training through reliable labels • Ensure structured layouts were represented correctly across batches

2022 - 2023

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Education

Work History