The talent gap is often the talking point in the industry. To discuss the typical analytics hiring scenario in India and steps that can be taken to bridge the talent gap, Analytics India Magazine caught up with Nitin Seth, CEO of Incedo Inc., who shares that talent gap is primarily driven by the sharp rise of analytics AI-based solutions needed in different industries. “The supply side has not been able to cope up,” he said.
Steps That Can Be Taken To Bridge The Gap
Sharing his thoughts on how it could be bridged, Seth notes three important steps that could be taken to deal with it.
- First is the strong collaboration between industry and academia. This could be facilitated by industry groups like NASSCOM, where industry representatives help in developing the right skill framework and the academic institutions enable the execution of it. Premier institutions like ISB and IIM Bangalore, along with companies in education and training sector are offering business analytics programs and certification in analytics, machine learning and AI, which could be of great help.
- Second important thing is for the companies to recognise that different markets require different skill profiles. In Europe, for example, one can find a PhD in statistics who deeply understands the domain and is also able to engage with clients. A profile like this is however extremely rare in India or are very expensive. A more creative solution is to break this down into skills that are easier to hire and bundle them together. For example, create joint teams of MBAs (who understand the business and have a strong math background), data scientists (who can build the models), and slightly more junior resources (who can do the tasks related to data operations).
- Lastly, and very simply, look beyond the obvious. For finding good data scientists we need to look beyond the biggest statistics and economics colleges. These resources from smaller colleges perform exceedingly well in team structures. Another immense benefit of hiring from some of these institutes is that you get a talent pool that is more loyal and stays with the company longer.
Hiring Process At Incedo And The Skills Required
According to Seth, strong problem-solving skills is one of the most critical requirements in an analytics professional. “Problem-solving can’t be learnt through teaching or training; it can only be picked up by “doing”, he said.
Noting that they hire analytics talent globally across all levels at Incedo, they look for T-shaped profiles and a good mix of domain, problem-solving, statistical and engineering skills. Explaining the T-shaped profiles, Seth says that the horizontal bar on the T is a combination of problem-solving, statistical and strong engineering skills, and the vertical bar represents deep domain understanding and expertise. Often, in fresh or more junior talent, one finds a basic mix of the “horizontal” skill sets. The vertical (domain expertise) needs continued industry exposure and hence more time to develop.
Seth shares that while it is easier to assess statistical, engineering and domain skills, it is difficult to assess core problem-solving skills in a candidate. “At Incedo we use the case study approach to assess this. These case studies are usually past client cases that have been appropriately masked”, he said.
Currently, the focus of most training programmes and certification courses is on technical and coding skills, especially in Python and R, coupled with the use of basic statistical techniques. This is a good starting point and it’s heartening to see that these skills are gaining momentum and becoming mainstream. Additionally, young analytics professionals should focus on developing their problem-solving skills by working on different client engagements and being exposed to disparate business problems.
“At Incedo, we run apprenticeship programs that give our budding analytics talent the opportunity to work in key analytics projects and get nurtured under the able guidance of our analytics experts”, he said.
Advice For Analytics Professionals
Two differentiating factors that can make a candidate stand out in analytics industry are deep domain knowledge and strong problem-solving skills. Other skills such as statistical knowledge and AI/ML engineering skills are also important.
“Often the magic of analytics seems in building the statistical models, tweaking them and getting them to yield results. But the key is in structuring the right problem statement. This needs deep problem-solving skills. Once the problem is structured, analytics professionals need to apply the right context to this problem and give recommendations. This needs domain expertise and industry knowledge”, he said.
On A Concluding Note
Seth shares that analytics professionals are looking for challenging assignments and learning opportunities that would help them deepen their skill sets. When organisations are not able to provide quality assignments to these professionals is when they get caught in the salary, better opportunity and retention issues.
To summarise, organisations need to focus on providing great learning opportunities and have a solid apprenticeship model to ensure that their analytics talent is motivated and engaged.