A study by Great Learning highlights the trend of hiring in the data science space, the growing demand for data science professionals and the gap in skills in the industry.
Highlight the study
- 57 percent indicated that there was a huge gap at the entry level and believed that this could be filled by revising the high-performance curriculum.
- More than two-thirds of hiring managers see gender gaps across talent
- 40% of employers think formal education in data science is essential for a career in data science
- 46 percent of employers trust hackathon and employee referrals as their preferred method of recruitment
- Bangalore takes the pole position for the availability of data science talent, followed by Hyderabad, NCR, Mumbai and Chennai.
A study on recruitment trends in the analytics and data science domain, conducted by Great Learning, India’s leading EdTech company for professional and higher education, revealed that the industry is seeing a huge supply gap with 92 per cent recruitment in data science talent. Managers are facing shortages. The research also highlights emerging skills gaps in data science specialist skills such as NLP, artificial intelligence robotics and automation, machine learning, etc.
As a strong support for the growing mainstream of the data science domain, almost half of the employed managers surveyed think a formal qualification / degree in data science is essential for a career in data science. The study also found that the lack of gender equality is a problem in the data science domain as it is with the broader technology domain. As recruitment activities return to the pre-epidemic stage, the report captures many insights on recruitment trends, talent demand, skills gaps, and other recruitment challenges.
More than half of the surveyed recruitment managers represented the B2B enterprise, about 1/4 represented the B2C company and the rest included the government and others. Industrial sectors like BFSI had the highest representation (21 per cent) followed by IT / ITES, retail, e-commerce, telecom, engineering and manufacturing.
Talent gap in data science
According to the survey, there is a wide gap between the talent sought by enterprises and the vast talent pool that it provides. Of all the employer managers and leaders surveyed for this study, 92 percent confirmed that they saw a wide demand and supply gap between data science talent in India. 57 percent believe that the gap between supply and demand exists at the entry level / fresher level whereas 27 percent of hiring managers believe that there is a talent gap in the mid-level role of team lead and project management. And upskill both mid-career professionals. The biggest skill deficits in Natural Language Processing (NLP) are identified by 15 percent of hiring managers, followed by artificial intelligence (12 percent), automation (11 percent), computer vision (CV) (10 percent), and analytics (9). Percent) and machine learning (7 percent).
Is formal education necessary for a career in information science?
As companies around the world reconsider the importance of degrees in their recruitment process and increasingly emphasize a candidate’s skills and abilities, the study examines the opinions of Indian hiring managers when it comes to the importance of degrees in their recruitment process. Forty percent of surveyed recruitment managers consider a formal qualification / degree in data science essential for a career in data science, which underscores the mainstream of the domain.
Cities that offer the most employment opportunities
Bangalore has once again become the leading city for talent recruitment in data science, with 54 per cent employers and hiring managers choosing it as the preferred destination for hiring data science professionals. It is followed by Hyderabad, an emerging cyber hub where 15 per cent of hiring managers express their confidence in the city. The BPO and KPO hubs of Delhi NCR and Pune were preferred by 9 per cent and 6 per cent respectively, followed by Mumbai and Chennai, which were preferred by 5 per cent each.
The large gap between Bangalore and the rest of the cities indicates the excellence of the ecosystem which consists of a skilled talent pool, with opportunities to work and conduct extensive research in modern application and consulting projects of data science in both corporate and startups. Field.
Top recruitment methods of choice for employers in the data science space
Hackathon and recruitment events are the most preferred method among recruiters for recruiting candidates. Hackathon gives managers the opportunity to test candidates in real-life situations by mimicking an environment where candidates must find a given business case or solution to a problem. This procedure is followed by employee referrals, which 22 percent of respondents identified as the preferred method of recruitment. It follows common procedures like online job site and campus recruitment.
The existence of gender gap
Gender gaps remain a problem not only in the data science domain but also in the broader technology sector. 68 percent of hiring managers indicated that gender gaps exist across the talent pool. The reasons behind this include gender-based pay inequality in the data science space, low awareness of opportunities within the fair sex, and lack of adequate support from initiatives to ensure a work-life balance.
The report is based on rigorous preliminary research through a series of analyzes and a survey published for the recruitment of managers and data science leaders across data science functions. This was complemented by direct discussions with a few hiring managers to understand the hiring perspective across the functions of Analytics, Artificial Intelligence (AI), Natural Language Processing (NLP), and Computer Vision (CV). 57% of respondents from the study were from B2B background, 28.7% from B2C background and 14.3% from other enterprise departments (government, B2B and B2C).
In terms of industry, the highest representation of BFSI sector was 21 percent. This was followed by IT and ITES departments and e-commerce and retail, representing 15 per cent and 12 per cent of the pool, respectively. Telecom represented 11 per cent of the total respondents, while the remaining participating sectors were engineering and manufacturing (10 per cent), media and entertainment (8 per cent), consumer goods and electronics (7 per cent), technology (5 per cent), and automobiles. (4 percent).