Artificial intelligence and data science are two of the most important technologies changing the way we live and work. Investment in AI has surged in recent years from $12.75 billion in 2015 to $93.5 billion in 2021, and Zion Market Research forecasts that number will grow to $422.37 billion by 2028. reached and has proven economic importance.
Regarding jobs and roles, AI and ML engineers/programmers, data scientists are important in building AI tools. Although the terms data science and AI are often used interchangeably, it should be understood that data science is an important pillar and aspect of AI, and knowledge of it is essential for training and building AI models. It is also important to distinguish between data analysts and data scientists. The former implies structured data, while the latter focuses on structuring unstructured data for further use.
With the importance of AI engineers and data scientists increasing year by year, INDIAai surveyed the employment landscape to understand their journey and future prospects. Our research was conducted to enlighten our readers, especially students who want to pursue a career in his AI and data science field.
methodology
The data was cleaned, filtered, and processed to make it ready for analysis before digging deeper to uncover some interesting findings. Having done that, the data were broken down into multiple age groups and total years of experience. In terms of geographic spread, most of the respondents are from Delhi and her NCR, followed by Maharashtra and Karnataka. Among the 130 respondents, there was a balanced distribution between AI/ML experts and data his scientists. The respondent’s profile ranges from his AI/ML engineer and architect with a focus on NLP and computer vision to a professor at a renowned university with interests in research areas such as image processing and meta-learning.
We performed multiple analyzes to understand how educational attainment and additional training/programming courses impact job prospects in this field. To do so, we categorized the dataset by total years of experience, educational background, highest degree achieved, and courses taken, and analyzed their impact on salary packages and data science careers. Additionally, the data is sliced at multiple levels to analyze the results and draw interesting findings.
The survey focuses on the educational background and career paths of respondents in data science and AI/ML, but takes a softer approach when it comes to salary packages in this area. In this regard, rather than specifically asking about current salary packages, respondents were asked their opinion on expected average salaries for Data Scientist/AI/ML professionals. This approach has opened up avenues of multiple interpretations, but INDIAai he reflected only one.
Main findings
Among the many findings that the survey uncovered, INDIAai would like to point out some interesting findings.
- Engineers dominate the fields of data science/AI/ML.
- Nearly 70% of respondents who did not start their careers in data science/AI/ML were able to transition to this field after eight years of work experience.
- Familiarity with programming languages such as R, Python, and Java can act as a catalyst to start a data science/AI/ML career.
Comparison with other employment surveys
Research is always essential to understand the current scenario in any area/area or sector. Multiple studies are conducted at different levels with several objectives. Such goals may vary from time to time and from place to place. Recently, there has been an increase in research around the world to understand the journey of the data scientist and to educate others. One such notable survey is his Kaggle survey. INDIAai took inspiration from research and tailored it for purpose. Unlike Kaggle’s research, our research is limited in scope to one country, India, and our findings are supplemented by in-depth interviews with experts in the field.
important point
This research has provided a better understanding of the data science/AI/ML job market and the steps required to start/shift a career into this field. Here are some lessons that Team INDIAai learned from their research:
- There is a strong urge among freshmen without a data science background to steer their careers into the data science/AI/ML domain.
- There is a young workforce (ages 30-40) in this sector as the sector itself is still evolving
- Candidates with non-engineering backgrounds can progress into data science/AI/ML areas with prior training/courses.
- Experienced professionals in this field are well compensated financially
