Artificial Intelligence and data science are two of the most critical technologies changing how we live and work. Investment in AI has surged in recent years from $12.75 billion in 2015 to $93.5 billion in 2021, with market projections by Zion Market Research placing these figures at $422.37 billion by 2028, demonstrating the economic importance. 

Regarding jobs and job roles, AI & ML engineers/programmers and data scientists are critical in building AI tools. While the terms data science and AI are often used interchangeably, one must understand that Data Science is an important pillar and aspect of AI, and its knowledge is essential for training and building AI models. It is also important to distinguish between a data analyst and a data scientist. The former makes sense of structured data, while the latter focuses on structuring unstructured data for further use.

With the growing importance of AI engineers and data scientists over the years, INDIAai surveyed the job landscape to understand their journey path and the prospects that it holds in the future. Our survey was conducted to enlighten our readers, especially students who wish to make a career in AI and data science.

Methodology 

Before we delved deeper to highlight a few interesting findings, the data was cleaned, filtered and processed to make it ready for analysis. Once we did that, the data was bucketed into multiple age groups and the total years of experience. Regarding the geographic spread, most of the respondents are from Delhi-NCR, followed by Maharashtra & Karnataka. Of the 130 respondents, there was a balanced distribution between AI/ML professionals and data scientists. The profile of the respondents ranges from AI/ML engineers and architects focusing on NLP and computer vision to academic professors from reputed universities whose research interests include image processing and meta-learning.

We ran multiple analyses to understand how educational qualifications and additional training/programming courses influence job prospects in this field. For that, we bucketed the data set into total years of experience, educational background and highest degree achieved and the courses taken and analysed their effect on pay package and a career in data science. Furthermore, the data have been sliced and diced at multiple levels to analyse the results and come up with interesting findings.

While the survey focused more on the respondents’ educational background and career path in data science and AI/ML, it has taken a softer approach towards the pay package in this field. In this regard, the respondents were asked their views on the expected average salary of data scientists/AI/ML experts rather than explicitly asking about their current pay package. This approach has opened avenues for multiple interpretations, and INDIAai has reflected only one. 

Major findings

Out of the numerous findings that the survey revealed, INDIAai would like to point out a couple of the interesting ones:

• Engineers dominate the field of data science/AI/ML.
• Nearly 70% of respondents who did not start a career in data science/AI/ML could move to the field after eight years of work experience.
• Familiarity with programming languages like R, Python and Java could act as a catalyst to start a career in data science/AI/ML.

Comparison to other job landscape surveys

Survey has always been integral to understanding the current scenario in any field/domain or sector. Multiple surveys are undertaken at various levels with several objectives. Such objectives may vary over time and place. Off-late, there has been a rise in surveys globally to understand the journey of data scientists and enlighten others on the same. One such noteworthy survey is the Kaggle survey. INDIAai draws inspiration from the survey and has tweaked it to suit its objectives. Unlike the Kaggle survey, the coverage of our survey is restricted to one country, i.e., India, and in-depth interviews with experts in the field have supplemented our findings. 

Key takeaways

The survey has given a better understanding of the data science/AI/ML job market and the steps one needs to take to start/shift their career to this field. A few lessons that team INDIAai learnt from the survey are:

• There is a keen urge among non-data science background freshers to steer their career into the data science/AI/ML domain
• The presence of a young workforce (30-40 years) in this domain as the field itself is still evolving
• Non-engineering background candidates can also make it to the area of data science/AI/ML with prior training/courses
• Experienced professionals in this field are well-rewarded financially

Download the high res version of the infographic here