Data Annotation (Data Labelling): A  Part-Time Job for Research Scholars

data labelling ( Data Annotation ) Career

I remember my visit to Singapore for a paper presentation at a prestigious international conference. The event was bustling with researchers from all over the world, each bringing unique insights and stories from their own journeys. Amidst all the excitement, one conversation stood out and changed my perspective on part-time jobs for research scholars.

After my presentation, I struck up a conversation with a research scholar from Australia. He specialized in the same field as me—image processing—and we instantly connected over our shared interest in the latest trends and developments in the domain. As we discussed our research, I asked him how he managed to support himself while pursuing his studies. His response surprised me.

He explained that while he received a stipend, it wasn’t quite enough to cover all his expenses in Australia, a country known for its high cost of living. To supplement his income, he had taken up part-time work in the field of data annotation, also known as data labelling. Given his background in image processing, he found it relatively straightforward to annotate images for various projects.

His job involved labelling data for machine learning models, helping them recognize specific objects, patterns, or outcomes. The work was flexible, allowing him to choose his hours and work from anywhere, even from the comfort of his home. This flexibility was crucial, as it enabled him to focus on his research while earning additional income.

I was intrigued. I hadn’t heard much about the data labelling industry until that point, and it was fascinating to learn how it was providing opportunities for researchers and students to earn extra money. He told me that the industry was growing rapidly due to the increasing demand for AI and machine learning applications across various sectors, from medical to automotive to finance. Companies and research labs often outsourced this work, creating a range of opportunities for skilled data annotators.

What struck me most about our conversation was the way this research scholar used his expertise in a practical setting. By working part-time in data labelling, he not only earned extra income but also gained valuable experience in the AI and machine learning fields. This experience, he explained, would be invaluable when he finished his studies and looked for full-time positions in industry or academia.

Our conversation opened my eyes to the potential for research scholars to find flexible, well-paying part-time work that complemented their studies. It also highlighted the evolving nature of research and technology, where new opportunities emerge to support the next generation of researchers.

If you’re a research scholar looking for ways to earn extra income while pursuing your studies, consider exploring the field of data annotation. It’s an industry with a growing demand for skilled workers and offers the flexibility and experience that can be invaluable in your career journey.


As a  research scholar, you face this dilemma at one point or another: the stipend you receive (if at all you receive one at all) just isn’t enough. You generally don’t have a lot of time to contribute to an outside job but you need the money, so it’s key to find one with the highest possible hourly rate. 

The data annotation(data labelling) field is one such option that the research scholars can look into making that extra income easily while continuing their research with full focus. This field demands less mental work but a little mechanical task to earn.

With the development of language models, training methods, AI tools, etc., there has been an increase in the demand for experts in data annotation. An important stage in supervised machine learning is data annotation, which is the process of labelling data to educate AI and ML models on how to recognize particular data categories and produce pertinent output. Applications for data annotation can be found in a wide range of industries, including chatbot firms, finance, medical, government, and space missions.

Data annotation is a growing industry, and the demand for skilled data annotators is increasing rapidly. Research scholars who have a background in computer science, statistics, or any field that involves data analysis can use their skills and expertise to annotate data accurately. Moreover, data annotation jobs are flexible and can be done from anywhere, which makes it an attractive career choice for those who want to work from home.

The market for labelling data using AI and ML has recently shown exponential growth. The data labelling industry will increase from USD 1.67 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 25.1% from 2022 to 2030 predicts market research firm  Region, and Segment Forecasts, 2022 – 2030.

Data annotation can also be a great opportunity for research scholars to gain experience in the field of machine learning and AI. By working on different data annotation projects, they can learn more about the field and gain hands-on experience in training machine learning algorithms. This experience can be valuable when applying for jobs in the industry or pursuing further education in the field.

If you are interested in knowing more career related opportunities, visit my blog posts related to Research Careers for PhD Researchers and Research Scholars.

Various Data Labelling Types

The quality of a model depends on the data it is fed. To optimize AI/ML models, it is crucial to provide the highest possible data quality together with precise labelling.

Let’s examine the different categories of data annotations:

Data Labelling in Visual Form

Image annotation is the process of assigning labels to digital photographs, usually requiring human input but occasionally requiring machine assistance. In order to teach the computer vision model about the items in the image, labels are predetermined by a machine learning engineer.

The following are the key competencies necessary for visual data annotation:

Analytical mathematics: in-depth knowledge of ML libraries; programming languages like Python, Java, C++, etc.; image analysis algorithms; visual database management; understanding of dataflow programming; and familiarity with tools like OpenCV, Keras, etc.

Labelling for Audio Data

Natural language processing (NLP), transcription, and conversational commerce all use audio data labelling. Real-time responses to spoken inputs are provided by virtual assistants like Siri and Alexa: To produce appropriate responses, their underlying models are trained using massive vocal command datasets that have been labelled. Tech behemoths like Amazon Web Services, Microsoft, and Google are using services from startups like Shaip to annotate audio files.

The abilities needed in this field are:

Analysis of spectrograms: Thorough familiarity with ML libraries; Python, Java, C++, and more programming languages;

Management of the Auditory Database: Expertise with programs like Studio One, Audacity, Adobe Audition, and Cubase.

Labelling for Text

The written word is a key component of communication on a global scale, whether it be in business, the arts, politics, or pleasure. However, unstructured text data is difficult for AI systems to parse. The ability to classify text in photos, videos, PDFs, and files as well as the context inside the words is made possible by training AI systems with the appropriate datasets to understand written language. Chatbots and virtual assistants are two significant contexts for text data annotation.

The main competencies needed in this profession are:

Knowledge of computational linguistics: experience with machine learning; database management; proficiency with programming languages such as Python, Java, and C++; and familiarity with tools such as GATE, Apache UIMA, AGTK, NLTK, and others.

Data Annotation: A Sector with Strong Growth Prospects

The demand for data engineers, data analysts, data labellers, and data scientists is skyrocketing as a result of India’s burgeoning AI and data analytics industries. Specialists in data annotation should be skilled in a variety of areas, including machine learning and understanding technologies tailored to the type of annotations. Long periods of concentration, attention to detail, and the capacity to manage many components of the machine learning process are requirements of the job.

As per, the average annual pay for a Data Labeling Job in the US is $50602 a year. Glassdoor poll found that depending on the employees’ talents and experience, major firms like Siemens, Apple, Google, and others give packages of up to INR 7-8 lakhs per year.

The basic need for the efficient operation of any AI model is labelled data of high quality. Therefore, it is crucial that a secure and economical technique of data labelling be implemented right away.

Skills Required for Data Annotation (Data Labelling) Jobs

Data annotation requires a combination of technical and non-technical skills. Here are some of the skills that are required to become a data annotator (as per the inputs received by one of the leading data annotation professionals):

  1. Attention to Detail: Data annotation requires a high level of accuracy and attention to detail to ensure that the data is labeled correctly. Even a small mistake in labeling can have a significant impact on the accuracy of the machine-learning model.
  2. Domain Knowledge: Depending on the type of data annotation task, domain knowledge may be required. For example, if you are annotating medical images, you may need to have knowledge of anatomy or medical terminology.
  3. Technical Skills: Data annotation involves the use of various tools and software. Therefore, having a basic understanding of computer skills is essential. This includes knowledge of basic programming concepts, file formats, and data manipulation.
  4. Good Communication Skills: Data annotators need to be able to communicate effectively with their team members, project managers, and clients. Good communication skills are essential to ensure that everyone is on the same page and that the project is completed successfully.
  5. Ability to Learn Quickly: Data annotation tasks can vary greatly, and new tasks may require learning new tools, software, or domain-specific knowledge. Therefore, having the ability to learn quickly and adapt to new challenges is essential.
  6. Time Management Skills: Data annotation tasks may have tight deadlines, and therefore, having good time management skills is important. This includes the ability to prioritize tasks, meet deadlines, and work efficiently.

Overall, data annotation is a skill that can be learned with practice and experience. However, having a combination of technical and non-technical skills can help you become a successful data annotator.

How to Find Data Annotation Jobs?

Research scholars can find data annotation jobs. Here are some options to consider:

  1. Online Job Platforms: There are several online job platforms that list data annotation jobs, such as Upwork, Freelancer, and Amazon Mechanical Turk. These platforms allow you to create a profile and bid on jobs that match your skills and experience.
  2. Specialized Companies: Many companies specialize in data annotation and offer job opportunities. These companies may have job postings on their websites or job search engines like LinkedIn, Glassdoor, or Indeed.
  3. Networking: Networking with professionals in the machine learning and AI industries can be a great way to find data annotation jobs. Attend industry events, join online communities or forums related to machine learning, and build relationships with professionals in the field.
  4. Freelancing: Research scholars can offer their services as freelance data annotators to companies or individuals who require data annotation. They can create their profiles on freelancing platforms like Upwork, Freelancer, and Fiverr to showcase their skills and experience.
  5. University Career Services: Many universities have career services offices that can help students and alumni find job opportunities. They may have connections with companies that are hiring data annotators and can help you find relevant job postings.

It’s important to note that the job market for data annotation may vary depending on the location and industry. Therefore, it’s essential to research the market and industry before applying for jobs.

You can visit my previous blog post on  Outsourcing Research Data Labelling: Risks and Rewards for Researchers for more details on companies that are working in the domain of research data labelling.

How can Data Annotation Experience help Research Scholars?

Data annotation experience can benefit research scholars in several ways:

  1. Skills Development: Data annotation requires a combination of technical and non-technical skills, including attention to detail, domain knowledge, technical skills, communication skills, and time management skills. These skills can be applied to a variety of career paths, including research and development, software development, data analysis, and project management.
  2. Industry Experience: Data annotation experience provides research scholars with an opportunity to gain industry experience, which can be valuable when applying for jobs. It demonstrates that you have practical experience working with machine learning technologies and can apply your skills in a real-world context.
  3. Networking: Data annotation jobs can be a way to build connections in the industry. Through networking, research scholars may learn about new job opportunities, gain insights into the industry, and build relationships with professionals in the field.
  4. Career Advancement: Data annotation experience can help research scholars advance their careers by demonstrating their skills, knowledge, and experience. They may be eligible for more advanced roles in the industry, such as data analyst, machine learning engineer, or project manager.
  5. Entrepreneurship: Data annotation can also be an opportunity for research scholars to start their own businesses. They can offer data annotation services to companies or individuals who require data annotation for their machine-learning projects.

Overall, data annotation experience provides research scholars with a valuable skillset and industry experience that can benefit their careers in many ways.

What are the potential Career Paths for Data Annotators (Data Labellers)?

Data annotation can be a valuable entry point into the field of machine learning and artificial intelligence and can lead to several potential career paths. Here are some of the potential career paths for data annotators:

  1. Data Analyst: Data annotation can be a stepping stone to a career as a data analyst. Data analysts use tools and techniques to analyze and interpret large datasets, extracting insights and identifying patterns to inform business decisions.
  2. Machine Learning Engineer: Data annotation provides valuable experience working with machine learning models and algorithms. This can be an excellent foundation for a career as a machine learning engineer, who is responsible for developing and deploying machine learning models and algorithms.
  3. Data Scientist: Data scientists use statistical and machine learning techniques to analyze and interpret data, and develop predictive models. Data annotation can provide valuable experience in preparing data for analysis, which is a critical step in the data science process.
  4. Project Manager: Data annotation projects require strong project management skills to ensure that the work is completed on time, within budget, and to a high standard. Data annotation experience can be an excellent foundation for a career as a project manager.
  5. Software Developer: Data annotation projects often require specialized software tools and workflows. Data annotators may gain experience working with software development teams, which can be a foundation for a career as a software developer.
  6. Data Labeling Team Lead: With sufficient experience, data annotators may move into a leadership role, overseeing teams of data annotators and ensuring high-quality, consistent labeling practices.

Overall, data annotation can be a valuable starting point for a career in machine learning, data science, project management, software development, or data analysis.

Can Data Annotation be done Remotely?

Data annotation can be done remotely. In fact, remote data annotation is becoming increasingly common, as it allows companies to tap into a global pool of annotators and reduces the cost and logistical challenges of managing an on-site team.

Remote data annotation can be done using various collaboration tools such as video conferencing, messaging apps, and project management software. Remote annotators typically receive training and guidance on the labeling guidelines and tools, and communicate with project managers or team leads to ensure that the work is completed to a high standard.

However, there are some potential challenges to remote data annotation. Communication can be more difficult when working remotely, and there may be a lack of oversight, which can lead to quality control issues. It is essential to have robust workflows and quality control measures in place to ensure that remote annotators are following guidelines and producing high-quality labeled data.

Overall, remote data annotation is a viable option, and many companies are successfully managing remote teams of annotators. However, it is important to have clear guidelines, workflows, and quality control measures in place to ensure that the work is completed to a high standard.

Is Data Annotation Different from Data Labelling?

Data annotation and data labeling are two terms that are often used interchangeably, but they do have some subtle differences.

Data annotation typically refers to the process of adding metadata or additional information to a dataset to make it more useful for machine learning or data analysis. This can include tasks such as identifying and marking objects in images, segmenting text or speech or providing descriptive tags to help categorize data.

Data labeling, on the other hand, is typically focused on categorizing or assigning labels to data. This can include tasks such as identifying whether an image contains a cat or a dog, labeling sentences as positive or negative sentiment, or tagging documents with relevant keywords.

In general, data annotation tends to be a broader term that includes tasks related to both labeling and adding additional metadata to data. However, both terms are often used interchangeably in the context of machine learning and data analysis.

Regardless of the specific terminology used, both data annotation and data labeling are critical tasks for preparing data for machine learning and data analysis. They help ensure that the data is properly categorized, labeled, and annotated to make it more useful for downstream analysis and modeling.

Popular Data Annotation Software Tools

Here are a few examples of software tools that can be used for data annotation jobs:

  1. Labelbox: Labelbox is a popular data annotation platform that provides tools for labeling images, text, and video data. It includes features such as collaboration tools, quality control measures, and integration with machine learning workflows.
  2. Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a managed data labeling service that provides tools for labeling image, text, and video data. It includes pre-built templates for common labeling tasks, as well as features for creating custom workflows and quality control measures.
  3. Google Cloud AutoML: Google Cloud AutoML provides tools for creating custom machine learning models, as well as tools for data preparation and annotation. It includes features for image and text labeling, as well as tools for data cleaning and preparation.
  4. Scale AI: Scale AI provides data annotation services for image, video, and text data. It includes a large pool of annotators that can be tapped into for labeling tasks, as well as features for quality control and data management.
  5. is a data annotation platform that provides tools for labeling images, text, and video data. It includes features such as automated annotation tools, collaboration tools, and integration with machine learning workflows.

The Table below provides a high-level comparison of some popular data annotation software tools based on various factors, including the type of software, supported data types, annotation types, collaboration features, quality control measures, and integration capabilities. However, it’s important to note that this is not an exhaustive list, and there may be other software tools available that are better suited for specific use cases.

SoftwareTypeData TypesAnnotation TypesCollaborationQuality ControlIntegration
LabelboxCloud-basedImage, Text, VideoObject detection, Classification, SegmentationYesYesYes
Amazon SageMaker Ground TruthCloud-basedImage, Text, VideoObject detection, Classification, Semantic segmentationYesYesYes
Google Cloud AutoMLCloud-basedImage, TextImage classification, Object detection, Entity extractionYesYesYes
Scale AICloud-basedImage, Text, Audio, VideoObject detection, Classification, Transcription, TranslationYesYesYes
Supervise.lyCloud-basedImage, Text, VideoObject detection, Classification, SegmentationYesYesYes
VGG Image Annotator (VIA)Open sourceImageObject detection, Classification, SegmentationNoNoNo
RectLabelDesktopImageObject detection, Classification, SegmentationNoYesNo
DataturksCloud-basedImage, TextObject detection, Classification, Entity extractionYesYesYes
ProdigyDesktopTextText classification, Named entity recognitionNoYesYes
Annotation.SpaceCloud-basedImage, Text, Audio, VideoObject detection, Classification, Segmentation, TranscriptionYesYesYes
Popular Data Annotation Software Tools

Challenges Involved in Data Annotation Career

Data annotation is a crucial step in machine learning projects, but it can also be a challenging process. Here are some of the challenges involved in data annotation:

  1. Subjectivity: Data annotation can be subjective, as different annotators may have different interpretations of the same data. This can lead to inconsistencies in the labeling, which can affect the accuracy of the machine-learning model.
  2. Quality Control: Ensuring the quality of the annotated data is essential. However, it can be challenging to ensure that all annotators are following the same guidelines and producing consistent results. Quality control measures such as spot-checking, inter-annotator agreement tests, and regular feedback sessions are essential to maintaining high-quality data.
  3. Data Volume: Some machine learning projects require a large amount of annotated data, which can be time-consuming and costly. Handling large volumes of data can also be challenging, as it may require specialized tools or software.
  4. Data Privacy: Annotators must handle sensitive or private data, such as medical records or personal information. Ensuring the privacy and security of this data is essential and may require compliance with data privacy laws.
  5. Annotator Bias: Annotator bias can occur when annotators inadvertently introduce their own biases into the labeling process. This can lead to inaccuracies and affect the accuracy of the machine-learning model.
  6. Lack of Domain Knowledge: Depending on the type of data being annotated, annotators may require specific domain knowledge, such as medical terminology or legal concepts. Without this knowledge, the labeling may be inaccurate or inconsistent.

Addressing these challenges is essential to ensure that the annotated data is of high quality and that the machine learning model accurately reflects the underlying data.


Data annotation has become an important aspect of machine learning and AI research, as the accuracy and reliability of machine learning models depend on high-quality annotated data. Research scholars who are looking to gain practical experience and earn an income can explore opportunities in data annotation, which involves labeling and organizing data sets for use in machine learning algorithms.

To become a data annotator, one needs to have strong attention to detail, communication skills, and the ability to work with various data types. There are many software tools available that can be used for data annotation, ranging from open-source options to cloud-based solutions with advanced features and integrations.

Data annotation experience can be beneficial for research scholars looking to build a career in data science, AI, or machine learning. The skills and knowledge gained through data annotation can help them understand the practical aspects of data preparation and preprocessing, and provide valuable experience for working on machine learning projects.

While data annotation can be challenging, with the right skills and tools, it can be a rewarding career path for research scholars who are passionate about data and interested in exploring the intersection of data and machine learning.

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Dr. Vijay Rajpurohit
Author: Dr. Vijay Rajpurohit
Dr. Vijay Rajpurohit is a researcher in Computer Science. He loves to educate researchers and research scholars on Research Paper Writing, Thesis Writing, Research Grants, Patenting Research Work and the latest Research-related issues. You can reach him @ [email protected]

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