Why is analytics hiring so hard?
“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. Image: AIMResearch With such lucrative job opportunities available, the recruitment for finding the right analytics professionals has become extremely tough. Recruiters are facing various challenges that can crop up when one is hiring for such an in-demand field. All that shines is not gold The analytics sector is extremely lucrative if you have the right blend of skills. Job seekers have realised 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. Image: LinkedIn “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 analyse 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 modernisation, 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. Ultimately you are solving a business problem A data scientist does not work in isolation. A data scientist has to ultimately solve a business problem through analysing data and building algorithms. Ankur Bhandari, global head of People Analytics at ABB, feels that a good understanding of business logic is missing in many analytics professionals. He says, “India has fantastic programmers in the country. As a data scientist, along with the programming, it is equally important to understand how a business creates value and generates revenue. Another area to work on is communication skills. Communication does not mean how well you speak a particular language but how well you communicate your ideas and make them understandable to others. You can go places if you master this skill.” “Finding the right talent with who is strong on data Analytics and is equipped with the relevant Domain knowledges has becomes a huge challenge lately,” feels Manisha (Sharma) Prasad, senior vice president & head of Human Resources for CRIF Companies in India. As a financial service organisation, on one side, we need strong functional and domain skills, but on the other side, it is crucial that we have data scientists and analysts on board who have the know-how to integrate tech skills and business knowledge. Manisha informs that the analytics space continues to reflect a gap for the talent blended in the two. Adaptability and learnability is the key “One has to accommodate and be flexible enough to get one of these two parameters and train people on the other parameter,” she concludes. Retaining talent “My dropout percentage in data analytics, data science and data architects is 25 to 30 per cent and that is my biggest concern,” says Amitabh. By now, it is quite clear that the demand for data science professionals exceeds the supply. As a consequence, 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. The report adds that 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. Image: AIM Research With startups coming up on  a monthly basis, especially in areas like Delhi NCR, and Bangalore, attracting talent is quite difficult. Dropouts are rampant Amitabh adds 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 observes that people are just changing jobs extremely frequently. 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.” Multiple offers is great, but do not use this as a tool to exploit Manisha 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 its important for them to have a clear insight on 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.” Not sustainable in the long run 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 pockets of many of the companies. They will become expensive hires for them. They will have to adjust to more stable companies. “Investors have started putting pressure to reduce their cost. We are seeing layoffs frequently. Companies will obviously try to bring down the business cost,” Ghosh concludes.
The attrition rate in the data science market stands at 28.1% in 2021, a 12.1% increase compared to 2020.
The attrition rate in the data science market stands at 28.1% in 2021, a 12.1% increase compared to 2020.
["AI hiring", "analytics hiring"]
["data science", "Go", "API", "machine learning", "AI", "analytics hiring", "Python", "Aim", "deep learning", "AI hiring", "analytics", "R"]
["AI", "machine learning", "deep learning", "data science", "analytics", "Aim", "Python", "R", "Go", "API"]
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good.

Reach me at sreejani.bhattacharyya@analyticsindiamag.com