Upskilling And Being Curious Are The 2 Keys To Transition Into Analytics, Says Sahana Shetty Of ANZ



With more than 17 years of experience in human resources department across global organizations in Software Product Development , Banking, Entertainment & Consulting , Sahana Shetty has helped organizations build unique cultures. Currently, Sahana is the HR Leader for Technology at ANZ. Her forte includes Digital and Agile transformation, HR Analytics, HR Consulting, and Leadership Development.

Here, Sahana tells us about the trends and buzz surrounding the vibrant field of analytics, and what goes into being a well-versed data scientist.

Analytics India Magazine: Is ‘Data Scientist’ the sexiest job of the 21st century?

Sahana Shetty: That’s debatable. This was actually quoted by the Harvard Business Review way back in 2012. They analyzed some interesting data points from LinkedIn and other companies. Reid Hoffman the co-founder of Linkedin , believed in the power of analytics. He empowered his team to not follow the traditional product development lifecycle but release short ads which will have a bigger impact on revenue. One of them were pop-ups, which increased the footage by 30 percent and had a direct impact on the revenue. Similar trends were noticed across organizations  like Google, Facebook and Amazon.

Over the years, the role of data scientist has evolved. If you look at the McKinsey report, it was written very well in terms of the need for a data scientist that can be questioned, especially in the business coming out there. The role of a business translator comes more into the picture, someone who bridges the gap between data science and business. That’s how this ecosystem has evolved.

While it is important to have data scientists, however there is a need to have various skill sets — be it in AI and machine learning as the industry is evolving. There is a talent crunch and there’s always a solution to it. When there is a problem, the ideal solution from a technology company is to introduce ‘everything as a service’. Data and machine language is now a service too. Keeping in mind the talent requirements and the infrastructure, companies like Amazon came up with Amazon Web Services (AWS), Google has its AI platform and IBM has Watson. These empower service organisations, for that matter, even startups who would not have the capability or infrastructure to depend on this as a service. So, the industry has evolved over the years.

AIM: There is a lot of buzz about analytics, big data and AI around the industry. Do you think these are mere buzzwords or are we actually seeing an analytics revolution in IT?

SS: Let’s consider the banking industry. ANZ is focused on creating a very simple banking experience for our customers. It’s important for us to develop tools or services that are more personalised, differentiated and provides a better customer experience. So, AI plays a critical role in this transformation we at ANZ are going through currently.

Now if you look back, there was a study done by IBM where they reached out to Executives to understand the impact of AI. It was interesting that 97 percent of the Executives felt that AI is going to be disruptive and 96 percent of them did contribute to AI saying that they are going to go ahead and invest in cognitive capabilities, which is again a huge percentage.

AIM: Do you think Data Scientists are expensive and difficult to find?

SS: Yes, definitely. Data science is a niche skill. When you look at the industry, data scientists are paid a premium compared to traditional engineers. At this point in time, we are looking for professionals who have a business mindset and understand the nuances of the business. This is challenging!

AIM: Do you think there is an imbalance between the available talent and required skill set in the analytics industry?

SS: Yes, there is an imbalance. Reportedly, for every ten professionals moving out of India, there are four professionals coming to India. So, there is a migration happening in the community which is a problem.

How do we fix this? There’s an interesting quote by Jeff Weiner in one of his interviews. He said that it’s critical to constantly acquire new skills to help prepare for the jobs that will be and not just the jobs you have. It is interesting from an employee point of view. As per a study, about 60 percent of the employees are unhappy, they think their skills are getting redundant. They are putting their time and money to upgrade their skills, which frankly, is a big positive if you need to build a learning organisation.

What does the industry do in the meantime? Basically, within the industry too, as per Zinnov’s report, companies like Microsoft and Accenture have accelerator programs that they are running to help startups not just in India but Internationally. Oracle also did a similar program in AI and ML.

AIM: We see institutes advertising saying that a candidate may get five or ten times his/her salary by switching to a career in Analytics. Do you think that’s just an advertising gimmick or does industry really appreciate its analytics talent more?

SS: Well, in terms of any publications, I would say it’s a gimmick. Personally, I do not believe in it. This is because there can’t be a number against a particular skill set. It is dependent on the individual and the knowledge that the individual possesses. There are various factors as an organization we look into.

AIM: What are the skill sets that companies are mostly looking at while hiring analytics talent?

SS: At ANZ, we not only look at the technical skills of the individual, but we also look at the person’s business acumen. We are keen to have individuals who are high on potential, who exhibit a growth mindset and have an inclination to learn and grow. Across the industry, in the case of Data Science, along with ML , analytics, data mining and Python programming are important skills to have.

AIM: Do you think a postgraduate degree or a specialisation course provides an advantage for getting hired?

SS: I think it’s a myth that a postgraduate degree will be an advantage for getting hired. Companies do look out for postgraduates with exposure to machine learning. What’s important for these organizations is a business acumen and understanding the business which is equally important.

So, is a postgraduate degree a way into the organisation? I would say no. It would be good for you to understand the business context. One of the best approaches would be working at a startup because you get end-to-end exposure to understand the entire product, and not just data science by itself.

AIM: What are the various initiatives that companies and educational institutions can take to set right the analytics talent flow?

SS: As per Zinnov’s report on AI and ML ecosystem in India, organisations like Amazon, Microsoft or Facebook setting up centers in India have contributed towards building an ecosystem. In fact, IISc in India. has a research lab dedicated to AI. What was most interesting here, was a recent publication from Intel where it set out to train 15,000 engineers in 2017, but managed to train about 99,000 developers, professors and students instead.

AIM: What are the three skills that you look for while hiring a candidate?

SS: Along with technical skills, we look for passion, someone who has a growth mindset and understand the business. So, these are areas that we look out for. As per industry experts, just hiring a data scientist or an engineer is not enough. Managers need to take special care to align business and data teams thus enabling data scientists to be self-sufficient. Otherwise, they might not get the expected ROI in data science which is a problem 70-80 percent companies face.

AIM: What would you advise freshers who are looking to start a career in Analytics?

SS: Be passionate about what you are looking out for, know your skills and understand them. It is also important that you do your background homework. What I mean by that is, understand the domain/industry that you are more inclined to, understand which organisations that you would want to look out for an opportunity.

Don’t look out exclusively at big brands. It doesn’t work that way. There are interesting startups in the ecosystem out here. Look at startups too because the exposure you get here is end-to-end which is really amazing. The second bit here is once you finalise what jobs you are going to target, it is important to go ahead and look at the job description. Don’t send random resumes to the recruiter. That doesn’t work either. Structure your resume in terms of job description, let it talk about your passion, skills and the reason or interest towards this job.

If there are additional certifications that really help, follow up with the recruiter post the interview to get feedback in terms of where it could have gone well. Another area where we lack is making use of our professional network. If you stepped out of college recently, I’m sure there is an alumni network that you can reach out to. Talk to these individuals and understand what is happening in their space.

Also, if getting hired is a challenge, a good alternative is to look at organisations who are working in the space of AI/ML, and join in as an engineer to understand roles better. I also recommend to go ahead with those certifications or institutes and upskill yourself. For example, if you look at Coursera, there is an interesting training on ML by Stanford University. You can look out for other courses similar to Coursera.

AIM: What is your advice to experienced professionals, who are now looking to transition into analytics.?

SS: In terms of transition to analytics, it is not easy. If you apply for roles outside your organisation, relevant experience is required. The alternate route I would recommend is looking within the organization. I’m sure there are teams working on AI/ML space, look for learning opportunities within these teams. Before you go ahead and approach your manager or the training department it is important understand your passion and your skills.

Learn and upskill yourself, which I think is the most important move. In case there are no opportunities within the organization, look at startups. India has about 75 percent of AI startups.  Experienced professionals or technology folks should invest time in networking and understanding what other organisations are doing in this space.

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Abhishek Sharma
I research and cover latest happenings in data science. My fervent interests are in latest technology and humor/comedy (an odd combination!). When I'm not busy reading on these subjects, you'll find me watching movies or playing badminton.

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