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Shaleen Atieno

Shaleen Atieno

Data Analyst Intern (Data Labeling & Classification)

Kenya flagNairobi, Kenya
$20.00/hrEntry LevelOtherLabelbox

Key Skills

Software

Other
LabelboxLabelbox

Top Subject Matter

Social Media and Digital Data Analytics
Election-related Hate Speech Detection
Consumer Sentiment and Social Media Analytics

Top Data Types

TextText
AudioAudio

Top Task Types

ClassificationClassification
Computer Programming/CodingComputer Programming/Coding

Freelancer Overview

Data Analyst Intern (Data Labeling & Classification). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, Egerton University (2025) and Kenya Certificate of Secondary Education, Bishop Okoth Girls Mbaga Secondary School (2021). AI-training focus includes data types such as Text and labeling workflows including Classification.

Entry LevelEnglishSwahili

Labeling Experience

Data Labeler (2025 Protests in Kenya Project)

OtherTextClassification
For the 2025 Protests in Kenya project, I labeled and categorized text data related to protest narratives to analyze public sentiment and thematic trends. This involved classification of sentiment and theme for deeper trend analysis of ongoing events. My labeling enabled the extraction of actionable insights from fast-evolving social and political contexts. • Labeled protest-related narratives by sentiment. • Categorized by themes relevant to protests. • Supported rapid analysis of current events. • Enhanced dataset structure for reporting and visualizations.

For the 2025 Protests in Kenya project, I labeled and categorized text data related to protest narratives to analyze public sentiment and thematic trends. This involved classification of sentiment and theme for deeper trend analysis of ongoing events. My labeling enabled the extraction of actionable insights from fast-evolving social and political contexts. • Labeled protest-related narratives by sentiment. • Categorized by themes relevant to protests. • Supported rapid analysis of current events. • Enhanced dataset structure for reporting and visualizations.

2025 - Present

Data Labeler (ACLED Conflict Project)

OtherTextClassification
In the ACLED Conflict Trends Analysis (Africa) project, I ensured the consistency and accuracy of conflict data through the correction of mislabeled entries and standardization of categories. My labeling focused on classifying event types such as attacks, riots, and protests for cross-country analysis. This work improved data quality for the subsequent analytical and reporting activities. • Standardized categories for conflict event datasets. • Corrected mislabeled entries on conflict data. • Maintained high dataset consistency. • Supported analytical and reporting workflow with accurate labels.

In the ACLED Conflict Trends Analysis (Africa) project, I ensured the consistency and accuracy of conflict data through the correction of mislabeled entries and standardization of categories. My labeling focused on classifying event types such as attacks, riots, and protests for cross-country analysis. This work improved data quality for the subsequent analytical and reporting activities. • Standardized categories for conflict event datasets. • Corrected mislabeled entries on conflict data. • Maintained high dataset consistency. • Supported analytical and reporting workflow with accurate labels.

2024 - Present

Data Labeler (Consumer Research)

OtherTextClassification
As part of consumer research projects, I extracted and categorized social media conversations focusing on customer sentiment and behavioral patterns. I conducted labeling tasks to assign sentiment and identify key themes such as complaints, praise, or questions. The work enabled more targeted social media analytics and marketing insight generation. • Categorized conversations using sentiment and topic analysis. • Supported labeling for customer behavior trends. • Maintained labeling accuracy and reliability. • Collaborated with teams to improve label taxonomy.

As part of consumer research projects, I extracted and categorized social media conversations focusing on customer sentiment and behavioral patterns. I conducted labeling tasks to assign sentiment and identify key themes such as complaints, praise, or questions. The work enabled more targeted social media analytics and marketing insight generation. • Categorized conversations using sentiment and topic analysis. • Supported labeling for customer behavior trends. • Maintained labeling accuracy and reliability. • Collaborated with teams to improve label taxonomy.

2024 - Present

Data Labeler (Election-Watch Project)

OtherTextClassification
For the Election-Watch project, I scraped and labeled text data to support hate speech detection in election contexts. I annotated and classified content as hate speech or non-hate speech for the creation of training datasets used in models. This work was central to preparing high-quality datasets for machine learning model development and evaluation. • Built a hate speech lexicon tailored for elections. • Annotated text with guidance for model training. • Ensured data quality for misinformation detection. • Supported efforts toward responsible AI use in elections.

For the Election-Watch project, I scraped and labeled text data to support hate speech detection in election contexts. I annotated and classified content as hate speech or non-hate speech for the creation of training datasets used in models. This work was central to preparing high-quality datasets for machine learning model development and evaluation. • Built a hate speech lexicon tailored for elections. • Annotated text with guidance for model training. • Ensured data quality for misinformation detection. • Supported efforts toward responsible AI use in elections.

2024 - Present

Data Analyst Intern (Data Labeling & Classification)

TextClassification
During my internship at Ampitech Solutions, I performed data labeling and classification on large digital and social media datasets. I was responsible for identifying sentiment, themes, and trends within the data for structured analysis. My work directly supported the development of organized datasets for business insight generation. • Labeled conversations by sentiment using internal tools. • Categorized topics and recurring trends for thematic analysis. • Ensured high-quality, accurate, and consistent data labeling. • Collaborated with analysts to refine labels and annotation guidelines.

During my internship at Ampitech Solutions, I performed data labeling and classification on large digital and social media datasets. I was responsible for identifying sentiment, themes, and trends within the data for structured analysis. My work directly supported the development of organized datasets for business insight generation. • Labeled conversations by sentiment using internal tools. • Categorized topics and recurring trends for thematic analysis. • Ensured high-quality, accurate, and consistent data labeling. • Collaborated with analysts to refine labels and annotation guidelines.

2024 - 2025

Education

E

Egerton University

Bachelor of Science, Computer Science

Bachelor of Science
2021 - 2025
B

Bishop Okoth Girls Mbaga Secondary School

Kenya Certificate of Secondary Education, General Secondary Education

Kenya Certificate of Secondary Education
2017 - 2021

Work History

A

Ampitech Solutions

Data Analyst Intern

Nairobi
2024 - Present