Key Job Roles In The Upcoming Field Of Data Labelling

This article explores the various data labelling jobs available and the roles, responsibilities, and requirements of the job.

Data labelling companies work with diverse sectors and industries. For example, data labelling is required for adding inputs to autonomous vehicles, geospatial technology, medical AI, finance and insurance tech, retail and government. This has given rise to a lucrative industry and the emergence of data labelling firms. These days, data labelling jobs can be found littered all over our dashboard. Data labelling job listings are found on LinkedIn, Internshala, Glassdoor, etc. The top five firms in India that provide data labelling services are:

  • ZURU – founded in 2019, Bangalore, India
  • COGITO TECH – founded in 2011, Delhi, India
  • iMERIT – Based in West Bengal, India 
  • WISEPL – Founded in 2020, Kerala, India
  • TIKA DATA – Founded in 2017, Bangalore India

Data labelling is the process of annotating raw data with meaningful and contextually informative tags and labels. The process allows machine learning models to learn more about the raw data. The labelling process involves “data tagging, data annotation, classification, moderation, transcription, or processing.”

This article collates the three main types of available data labelling roles and the specifications required. 


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Computer Vision Data Analyst 

Computer visions are unable to recognize images without machine learning. Computer vision data annotation facilitates machine learning so that the computer can recognize the image that it sees. Computer vision data analysts label images, key points or pixels with precise tags. It is a time-intensive and labour-intensive process, fed to the machine in clean and accurate labels. Through drawing bounding boxes within an image, computer vision data analysts specify a particular characteristic/object within an image and label it. Image data annotation is similar to creating CAPTCHA images and also giving the response for it. 

Computer vision data analysts also label objects within videos, where they tag the object in each frame. This creates a computer vision model that can automatically categorize objects, images, etc. For example, Sizzle, a company based in Bangalore, is building AI to automate gaming highlights from Twitch and YouTube. For this purpose, they require a data labelling analyst who gathers screenshots, clips and other data from gaming videos and labels them. 

Data annotation firms that provide data labelling services and tech giants like Amazon Web Services employ computer vision data analysts. The skills required to carry out the role are:

  • Data entry
  • Machine vision
  • Artificial Intelligence (AI)
  • Data Analytics
  • Manual testing 

Linguistic Data Labelling Analyst

Linguistic data analysts are required to carry out natural language processing. This allows machines to identify text parts, the tonality of text, text within images, PDFs and files, and classify proper nouns. In addition, ML projects are used to create chatbots on different websites. 

Qualifications for a linguistic data analyst may range from undergraduate to MBA. Based on the type of company hiring, the analyst must hold complex language knowledge of a specific type of jargon (eg. business, developmental, public policy). For example, companies like J.P Morgan Chase, AWS, and Apple hire data labelling analysts well-versed in complex jargons. 

Audio Data Labelling Analyst 

Virtual assistants like Alexa and Siri are trained through machine learning to process vocal commands. Each business trains its virtual assistants and chatbots, for which audio recordings are created, annotated and analyzed. The tone, intent and literal meanings of the words are carefully filtered to get the desired response from the virtual assistant. Adding tags and categorizing audios facilitates deep and quick ML as they become training sets. 

An audio data labelling analyst creates voice recordings, transcribes them, analyses the recording and ensures quality of the audio, before transferring the data to the model for machine learning. 

Firms like Shaip provide auditory data labelling services for featured clients like Amazon Web Services, Google, and Microsoft. Audio data analysts are well sought after by companies like Telus International, Amazon Data Services, Open AI etc.


As per the key finding in a report by NASSCOM, the data annotation market in India was estimated to be USD 250 in 2020. The report iterated that the MSP and BMP business models, pillars of cost, infrastructure, talent, and innovation provide the optimal conditions for a “dedicated task force” in India. Further, the report states that 75% of the data annotating industry is still in its initial growth phase. 

Data labelling firms are making a positive social impact on their employees as well. As per the information provided on the iMerit website, the company boasts of a workforce with over 52% female workers. Additionally, iMerit has helped the family income of data label service providers to triple over the years. Cogito Tech, another data labelling firm, works with over 1100 employees, 150 clients and annotates data in 35 languages over 25 countries. 

India has become a top contender for the data labelling job market globally, fuelling the AI ecosystem. The data annotation service in the country is projected to be evaluated at USD 7 billion by 2030. 

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Abhishree Choudhary
Abhishree is a budding tech journalist with a UGD in Political Science. In her free time, Abhishree can be found watching French new wave classic films and playing with dogs.

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