“Rohit bagged a 50 lakh job as a data scientist,” “Meena received a pay package of 25 lakhs as a data engineer,” sounds familiar? Well, pick up any newspaper; this is a common headline media outlets use to grab your attention. And most of the time, they work. Earlier, it used to be just for software engineering jobs. Now, analytics jobs have come into the picture. Analytics jobs are indeed sought after. The median salary of data science professionals in India has increased to INR 16.8 lakhs per annum, up by 25.4% compared to 2021, as per a recent study by AIMResearch.
The lucrative analytics sector offers some of the best job opportunities in the country, with data scientists, data engineers, and analytics professionals in high demand. However, finding the right talent for these jobs can be a daunting task for recruiters. They face various challenges that can crop up when one is hiring for such an in-demand field. In this article, we will discuss the glamour and challenges of analytics jobs in India.
All that shines is not gold
The analytics sector is extremely lucrative if you have the right blend of skills. Job seekers have realized that if they highlight the knowledge of in-demand skills like advanced analytics, deep learning, and machine learning, they can bag these high-salary roles. There is nothing wrong with putting them in your CV, but you have to actually deliver what you claim. A common complaint from recruiters is that often what is written on your resume may not really match up with your actual skills.
In a post last year, Nitin Aggarwal, head of Cloud AI Industry Solutions Services (India) at Google, highlighted this issue in a LinkedIn post, which has gained much traction.
“People put a lot of things on the CV, say, knowledge of advanced Python coding, but give them a real-life coding scenario to test their experience level, they will not be able to match up to the expectations. We are investing a lot to get technical assessment tools to analyze these kinds of issues,” adds Amitabh Ghosh, head of Talent Acquisition at Anheuser-Busch InBev.
Obsession with tools
Jaidev Dutta, executive director at a Big Four consulting, adds, “Candidates are more focused on showcasing their expertise in tools and technologies and not the fundamentals of how an analytics project has to be delivered. Tools and technologies will keep on changing but understanding how a data and analytics project has to be delivered and how to build a robust solution is what is missing. This is the case not only at the entry-level but also at the mid-senior level.
Getting the right mix of skills needed
A good analytics professional needs a diversity of skills to solve a business problem through data science. This is where recruiters stumble upon another roadblock to finding professionals not just proficient in one skill but someone who can meet all the client requirements.
“Unlike other tech domains, the complexity of skills in data analytics is huge. It starts with data ingestion, data architecture and data modernization, advanced analytics, and information delivery. In each of these areas, there are a plethora of tools and technologies. It’s a rapidly changing stack as well – finding the right combination of skills in a single candidate. Even candidates find it challenging to keep up with changing tech platforms all the time,” adds Jaidev.
Dutta adds he has figured out that maybe we can never get candidates with a combination of all the skills that we want with the right expertise level (or get very few in numbers). In order to solve this issue, Dutta says the company is trying to focus on certain base skills that are mandatory for a particular job role and then trying to cross-train and upskill them in other areas. This not only helps in building a more well-rounded analytics team but also improves employee retention as they see growth and development opportunities within the organization.
Retaining talent is another big challenge faced by recruiters in the analytics industry. Amitabh Ghosh, head of Talent Acquisition at Anheuser-Busch InBev, says, “My dropout percentage in data analytics, data science, and data architects is 25 to 30 per cent, and that is my biggest concern.” The demand for data science professionals exceeds the supply, and attrition rates in the analytics industry are quite high. The attrition rate in the data science market stands at 28.1% in 2021, a 12.1% increase compared to 2020, as per the Analytics India Attrition Study 2022 conducted by AIMResearch.
According to the report, Bangalore has the highest attrition rate among metropolitan cities, at 29.7%, followed by Mumbai (28.8%), Kolkata (28.1%), and Delhi/NCR (27.8%). Startups and boutiques have the highest attrition rates of 43.7% and 42.1%, respectively. With startups coming up on a monthly basis, especially in areas like Delhi NCR and Bangalore, attracting talent is quite difficult.
Amitabh Ghosh observes that these startups are cash-rich and are ready to buy out talent. The kind of increments these startups are making makes it difficult for normal companies to match up to. The same candidate which used to cost “x” maybe two years down the line is costing us “4x” or “5x” where x is their current salary. He adds that people are just changing jobs extremely frequently, and the stability part is missing in their careers. “You get good candidates sitting with multiple offers. Even if they get a good offer from us, they will go back to negotiate with the other employers and work out a better offer. We are seeing lots of dropouts on the day of joining. If you look at it from the industry perspective, the industry is looking at 30 to 40 per cent dropout.”
Manisha (Sharma) Prasad, Senior Vice President & Head of Human Resources for CRIF Companies in India, also agrees with Ghosh. Every other individual is sitting with 4 or 5 offers. “Having multiple offers is alright as demand exceeds supply. Using the opportunity to negotiate with multiple employers for counter offers is something that is not appreciated by the employers. Manisha also shares her thoughts for the candidates in the market, that it’s important for them to have a clear insight into their own needs and aspirations – momentarily appearing lucrative financial figures or career stability and exposure. The focus by and large has been on compensation, and long term and intangibles are often overlooked by the candidates in making their career choices. Even if I have five offers in hand, I need to be clear what it is that I am aspiring for – financials or career stability. I have seen in the last few months that the focus is purely on compensation. Long term and tangible and intangible benefits should be evaluated as well.”
With several professionals earning whopping amounts in the early phase of their career, which one could only dream of before, Ghosh feels soon, a time will come when these professionals will become out of reach of the pockets of many of the companies. They will become expensive hires for them, and investors will start putting pressure to reduce their costs. Ghosh concludes, “We are seeing layoffs frequently. Companies will obviously try to bring down the business cost.”
In conclusion, while the analytics sector is indeed lucrative, job seekers need to realise that simply showcasing knowledge of in-demand tools and technologies is not enough. It is essential to have a thorough understanding of the fundamentals of analytics and the ability to solve real-world business problems through data science. The industry needs analytics professionals who possess a diverse set of skills and can adapt to the rapidly changing technological landscape.
Furthermore, recruiters need to focus on cross-training and upskilling their employees to fill the skill gap instead of solely relying on finding the perfect candidate with all the required skills. Retaining analytics talent is also a significant challenge, with high attrition rates in the industry.
As the industry evolves, it is essential to evaluate career choices based not just on compensation but also on long-term stability and intangible benefits. While the analytics sector continues to offer lucrative job opportunities, job seekers and recruiters alike need to focus on developing a comprehensive understanding of analytics fundamentals and a diverse skill set to be successful in the field.
