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Raquel N. Souza

Raquel N. Souza

Data Engineering Analyst (Mid-Level), HST Card Technology

Brazil flagManaus, Brazil
$15.00/hrExpertOtherCVATToloka

Key Skills

Software

Other
CVATCVAT
TolokaToloka
Scale AIScale AI
RemotasksRemotasks
MercorMercor

Top Subject Matter

Generative AI Solutions
Large Language Models (LLMs)
Retrieval Augmented Generation (RAG)

Top Data Types

TextText
DocumentDocument
AudioAudio

Top Task Types

ClassificationClassification
SegmentationSegmentation
Text GenerationText Generation
Object DetectionObject Detection
RLHFRLHF
TranscriptionTranscription
Data CollectionData Collection

Freelancer Overview

Data Engineering Analyst (Mid-Level), HST Card Technology. Brings 8+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Certificate, Data Science Academy (2024) and Bachelor of Science, Centro Universitário UniFatecie (2023). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

ExpertEnglish

Labeling Experience

Data Engineering Analyst (Mid-Level), HST Card Technology

Text
Implemented Generative AI solutions using LLMs and SLMs involving prompt design, output assessment, and iterative refinement tasks. Led LLM output evaluation, including retrieval accuracy checks and structured response quality scoring, similar to RLHF workflows. Developed and validated anomaly detection models through ground truth data labeling and quality assurance processes. • Conducted rigorous model output validation by comparing predictions against labeled data. • Created detailed documentation for workflow guidelines and annotation standards. • Built RAG pipelines requiring relevance and context labeling. • Focused on quality and consistency in AI output assessment.

Implemented Generative AI solutions using LLMs and SLMs involving prompt design, output assessment, and iterative refinement tasks. Led LLM output evaluation, including retrieval accuracy checks and structured response quality scoring, similar to RLHF workflows. Developed and validated anomaly detection models through ground truth data labeling and quality assurance processes. • Conducted rigorous model output validation by comparing predictions against labeled data. • Created detailed documentation for workflow guidelines and annotation standards. • Built RAG pipelines requiring relevance and context labeling. • Focused on quality and consistency in AI output assessment.

2024 - Present

Business Intelligence Analyst, Data Ágil

OtherTextClassification
Conducted end-to-end annotation for structured and unstructured datasets, including data collection, cleaning, classification, and labeling. Applied quality control checks to annotation output, ensuring accuracy, consistency, and completeness. Iterated on data schemas and labeling taxonomies to align annotation with model objectives and improve performance. • Reviewed labeled data for predictive models using Python and scikit-learn. • Developed annotation guidelines and contributed to improved annotation QA processes. • Managed annotation pipelines for diverse BI and predictive modeling projects. • Emphasized annotation output tailored to business requirements.

Conducted end-to-end annotation for structured and unstructured datasets, including data collection, cleaning, classification, and labeling. Applied quality control checks to annotation output, ensuring accuracy, consistency, and completeness. Iterated on data schemas and labeling taxonomies to align annotation with model objectives and improve performance. • Reviewed labeled data for predictive models using Python and scikit-learn. • Developed annotation guidelines and contributed to improved annotation QA processes. • Managed annotation pipelines for diverse BI and predictive modeling projects. • Emphasized annotation output tailored to business requirements.

2022 - 2024

Data Analyst, RS Consultoria

OtherTextClassification
Labeled and categorized marketing campaign datasets for performance classification and trend analysis purposes. Tagged datasets consistently for various campaign types and time periods to ensure reliable reporting model input. Maintained annotation standards and contributed to campaign data quality improvement. • Labeled data from Meta Ads and Google Ads platforms. • Organized data annotation workflow for campaign analysis tasks. • Applied taxonomy and schema standards to ensure annotation consistency. • Supported downstream reporting and analytics with accurately labeled data.

Labeled and categorized marketing campaign datasets for performance classification and trend analysis purposes. Tagged datasets consistently for various campaign types and time periods to ensure reliable reporting model input. Maintained annotation standards and contributed to campaign data quality improvement. • Labeled data from Meta Ads and Google Ads platforms. • Organized data annotation workflow for campaign analysis tasks. • Applied taxonomy and schema standards to ensure annotation consistency. • Supported downstream reporting and analytics with accurately labeled data.

2019 - 2021

Education

D

Data Science Academy

Certificate, Data Engineering

Certificate
2023 - 2024
C

Centro Universitário UniFatecie

Bachelor of Science, Agrocomputing

Bachelor of Science
2023

Work History

H

HST Card Technology

Data Engineering Analyst

Manaus
2024 - Present
D

Data Ágil

Business Intelligence Analyst

Manaus
2022 - 2024