Vahiny Nirina Razafimahandry

Key Skills

Software

Appen

Clickworker

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Emotion Recognition

Evaluation Rating

Freelancer Overview

With a strong background in AI data labeling and digital marketing, I specialize in annotating multimodal data (images, text, audio, video) to train and refine AI models. My experience at Appen Connect has honed my ability to provide high-quality, accurate annotations, ensuring AI systems receive well-structured datasets for optimal performance. I am adept at reviewing and validating labeled data to meet industry standards. Beyond data labeling, I have a rich professional background in SEO, digital marketing, and virtual assistance. My analytical skills and keen attention to detail allow me to assess AI responses, ensuring accuracy and user satisfaction. My expertise in data analysis, content creation, and AI model evaluation makes me a valuable asset for projects requiring precision, efficiency, and reliability in training AI systems.

Labeling Experience

Mobile App Scenario Testing

AppenTextEvaluation Rating

As a Mobile App Scenario Tester for Appen, I evaluated the functionality, usability, and responsiveness of mobile applications by following predefined test scenarios. The goal of the project was to ensure that apps perform seamlessly across different devices and user interactions. Key tasks included: Executing scenario-based testing to assess app behavior in real-world conditions Identifying bugs, inconsistencies, and UI/UX issues affecting the user experience. Providing detailed feedback on navigation, accessibility, and performance Following strict quality guidelines to ensure reliable and actionable test results This project required attention to detail, analytical thinking, and a strong understanding of mobile app interactions. My work contributed to enhancing app usability and functionality, leading to a more seamless experience for end-users.

As a Mobile App Scenario Tester for Appen, I evaluated the functionality, usability, and responsiveness of mobile applications by following predefined test scenarios. The goal of the project was to ensure that apps perform seamlessly across different devices and user interactions. Key tasks included: Executing scenario-based testing to assess app behavior in real-world conditions Identifying bugs, inconsistencies, and UI/UX issues affecting the user experience. Providing detailed feedback on navigation, accessibility, and performance Following strict quality guidelines to ensure reliable and actionable test results This project required attention to detail, analytical thinking, and a strong understanding of mobile app interactions. My work contributed to enhancing app usability and functionality, leading to a more seamless experience for end-users.

2021

Search engine evaluation

AppenTextEvaluation Rating

As a Search Engine Evaluator for Appen, I assessed and rated search engine results based on relevance, accuracy, and quality. The project aimed to improve search engine algorithms by refining their ability to deliver highly relevant and user-friendly results. Key tasks included: Evaluating the relevance of search queries to displayed results Analyzing search intent and categorizing responses accordingly Identifying biases, inaccuracies, and inconsistencies in search outputs Adhering to strict quality guidelines to ensure high evaluation accuracy This large-scale project required meticulous attention to detail, linguistic expertise, and a deep understanding of user search behavior. My contributions helped enhance AI-driven search engines, ensuring more intuitive and precise results for end-users.

As a Search Engine Evaluator for Appen, I assessed and rated search engine results based on relevance, accuracy, and quality. The project aimed to improve search engine algorithms by refining their ability to deliver highly relevant and user-friendly results. Key tasks included: Evaluating the relevance of search queries to displayed results Analyzing search intent and categorizing responses accordingly Identifying biases, inaccuracies, and inconsistencies in search outputs Adhering to strict quality guidelines to ensure high evaluation accuracy This large-scale project required meticulous attention to detail, linguistic expertise, and a deep understanding of user search behavior. My contributions helped enhance AI-driven search engines, ensuring more intuitive and precise results for end-users.

2021

Sentiment Recognition in Video

AppenVideoEmotion Recognition

As a Sentiment Recognition Annotator, I analyzed video footage to identify and label human emotions based on facial expressions, body language, and vocal tone. The goal was to train AI models in emotion detection to improve applications in customer service, healthcare, and interactive AI systems. Key tasks included: Watching and annotating human emotions in video sequences (happiness, sadness, anger, etc.) Ensuring consistency and accuracy in sentiment labeling based on predefined guidelines Identifying subtle emotional cues and distinguishing between nuanced expressions Following strict annotation standards to maintain high-quality labeled datasets This project required keen observational skills, attention to detail, and a deep understanding of human emotions. My contributions helped refine AI models for emotion recognition, making them more accurate and context-aware in real-world applications.

As a Sentiment Recognition Annotator, I analyzed video footage to identify and label human emotions based on facial expressions, body language, and vocal tone. The goal was to train AI models in emotion detection to improve applications in customer service, healthcare, and interactive AI systems. Key tasks included: Watching and annotating human emotions in video sequences (happiness, sadness, anger, etc.) Ensuring consistency and accuracy in sentiment labeling based on predefined guidelines Identifying subtle emotional cues and distinguishing between nuanced expressions Following strict annotation standards to maintain high-quality labeled datasets This project required keen observational skills, attention to detail, and a deep understanding of human emotions. My contributions helped refine AI models for emotion recognition, making them more accurate and context-aware in real-world applications.

2022 - 2022

Education

Work History