randomized control trials

Randomized control trials(RCT) as described by John M.Last as

Top 10 Data Science Career Myth

Data Science, Machine Learning and Artificial Intelligence are gaining importance for their respective use. But many have their doubts before taking up these roles as their career. Data Science Career is one of the most s

Yash Chaudhury

As a part of our Industry collaboration, this week we caught up with Mr. Yash Chaudhary – the Founder of Zorba Consulting India. For him, it was always clear the path he wanted to charter. Even while doing his Engineering, Data Science always intrigued him and today he is a successful Consultant and Entrepreneur and a well-known name in the Data Science world.

He says “I don’t know if there will be too many languages coming up but I see Python being here for some time now.”

SK: First of all, I would like to sincerely thank you for devoting time for this interview. From a BTech in Chemical Engineering to the Data Science world. Would you please share your journey?

YC: For me, it was always very clear. In the first six months of my Engineering, I realized it didn’t interest me that much. But I had an enormous interest in Business Affairs, in extra-curricular activities in the campus, Sales, Marketing, Entrepreneurship things of that sort.

python

So, I had the first cut ready for me that I am not going into the core chemical Engineering domain but I am going to stay more on the business focus. As I spent more and more time on the campus, I came across different things which were pushing me more and more towards the business side.

I came to know about Analytics, around the 3rd year or the beginning of the final year and I remember the placement was due right after. I went for the presentation of one of the companies, Fractal Analytics. After looking at their presentation I was quite convinced that is exactly where I needed to be and joked about it to friends too that I will get placed on that day in that company. And voila, I started working for Fractal.

SK: When and how did you think that you will start your own consultancy? What were the market gaps in the Data Science world that prompted you to start Zorba?

 YC: Before starting Zorba, I had already worked for five years and in those five years I always felt, that although I was doing some really interesting work I never got to know how that was implemented.

Zorba Consulting India

Most of our clients used to be Fortune 500 and they had a huge hierarchical structure for decision making and everything that was proposed had to go through a lot of iterations and stages of approval through various stakeholders. But there was not enough visibility on the impact of the work I was doing. In the company I was working prior to starting Zorba was Fischer Jordan, a boutique consulting firm, it felt like a blessing in disguise to me as it was a small company with a small team of around four to five people in India and five in NY.

I got to know the entire end to end consulting gamut through this role as every individual was under the radar and everything from defining the analysis to selling the analysis to bringing in the ideas had to performed and to me it was a career-defining experience. Working directly with the partners having 20-25yrs of experience and co-founders of some companies gave me a different level of exposure and maturity. After three years of working there, I realized that even if I bring out the best of the solutions to the table, I would still be limited in terms of being it the reward that I would get or the growth I could achieve. As I was working with the partners, my fate was sealed for the next three or four years.

So I didn’t have any way to move up the ladder for at least next three to four years. Considering that I thought of starting my own practice where I will have control over the kind of work I do, I will have control over that rewards I make and I would have control over the impact that my work brings to the table and have visibility also. At that time, I felt that all the consulting work that was happening, it was majorly on recommendation part but then no one was getting their hands dirty with implementation or the program management part of the analytics that we offer. So that’s where I thought Zorba would be very important. And another critical factor that we had in mind was the smaller players like the SMEs and startups which cannot afford to hire consulting companies like Fractal or Musigma or the established analytics players. They can’t afford to spend that kind of money because their overall project budget is lesser than the minimum budget these companies aim to start an engagement. That was also an agenda in our mind that we want to help these companies because we felt we could make a difference.

SK: So when you hire a data science professional, what are the skills or qualities that you look for specifically?

 YC: So there are three to four key metrics that a star Data Scientist needs to kind of be strong at. Two of them are technical and two of them are non-technical or behavioral aspects. The most important one on the technical front is the math and statistics capability. So how strong one is with math and logical thinking and statistical knowledge in general. And how smart one is to bring out those highly technical concepts out into the real world to solve a problem or a puzzle or probability, how easily one can apply or how effortlessly does one see probability being applied across different aspects of your life. Second is the programming and coding aptitude in general.

Data Scientist

To perform any kind of analysis one would want to have certain competencies on the tools like saying R or Python or SAS and all of those required to have a very basic level of coding bent at least so I won’t say coding is very critical. But it is required.  From my experience, I have found that one cannot do without the non-technical skills. The first one is the business acumen in general. One’s way of thinking, how a problem is perceived, how to structure a solution, how does the mind break down the problem and how quickly information is assimilated.Although a slightly-definable trait I find that to be the number one parameter of being a successful Data Scientist and a successful professional as well. The business acumen, the capability to understand the bigger context, the capability to understand constraints across various stakeholders and the capability to understand the hierarchical challenges those kinds of things. The second critical skill is communication. To be able to clearly convey one’s thoughts to the end-users is quite an important task I believe.

SK: Absolutely. It actually corroborates the fact that experts say like the World Economic Forum list of 20 soft skills which professionals need to possess in 2020 along with the technical skills. So just another question on that front. How important is EQ you think in this digital age?

YC: Very important. It’s like one can know what code to write. One can know what the problem is. But then to solve any problem one needs to collaborate with people right. And to collaborate with people without a high EQ it would become tough for one to navigate across.

SK: What does the future hold for data science professionals or Data Scientist?

YC: I think this role is going to become very popular in the future. The future has to be driven towards product-based solution offerings. Soon we cannot remain in the service domain forever. It’s going to somewhere become counterproductive because the same kind of work one cannot keep replicating by putting more and more and more resources.

Future of data scientist

That is a little bit counter growth as well that when one gets more work, one brings more people and that model somewhere takes one to a dead end where it becomes really hard to the business or the company to scale up.

So it has to become a product-driven solution offering in the future. So it is critical for Data Scientists I think to merge the key skills with the product management skills Its about how one can create a product which can just be given away or installed onto the client premises and then that can take care of lots of those things.

SK: Do you think automation will act as a disruptor to the Data Scientist role?

YC: As I said the Data Science role is not that structured that automation can right away take things down the drain. But I do believe that almost about 50 percent of the tasks that a Data Scientist does today, those could be efficiently automated.  So maybe ten years down the line, we would not see the 50 percent of the tasks being done by a Data Scientist himself. Some tool will just take care of that entire thing for things like data cleaning, data transformation to a very large extent will be automated. But. how to drive the analysis and how to push the analysis in a certain direction; keeping in mind the intricacies of the business and keeping in mind lots of practicalities at the end of the various stakeholders that you deal with. That is something I feel is going to take a long time to be automated even with Artificial Intelligence. I know that it is progressing really fast but even with that it is going to take a long time to be replaced completely. So I don’t see any harm or any danger to the Data Scientist role at least for the next 10 years.

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SK: Around 5 years back nobody used to speak of Python and it was all about SAS or R or SPSS. Today it’s all about python and in the future maybe it will be something else. So with the progress of time do you think people have to keep learning these newer languages?

 YC: And that’s exactly why I said that there’s no point being an expert in a coding language but one should have a coding aptitude. R was a scripting language. But Python now has a multitude of possibilities. One can develop their own products using Python, it can be linked to lots of different interfaces, every database out there and then there’s so much more that one can accomplish with it. When I started my career, SAS was everything pretty much everything. But now the way Python has come up, it’s surprising to me as well. But the next five years I don’t think  anything is going to come which is completely going to like throw Python out of the gate because I feel like most of the things that you would want to accomplish like the shortcomings that I could perceive of with something like SAS or something like R or SPSS all of those shortcomings are very decently covered with the Python tool package. So I don’t know if they’ll be too many languages coming up further but I see Python as the being here for some time now.

SK: What message or advice would you have for the young professionals who are looked into with a career in AI or ML?

YC: I would say to understand the business problem really well. Rather than just focusing on technique-driven capability building. So, one should understand the problem. Once the problem is figured out, then a strong will is required to attack that problem. One should start from top-down rather than just learning every other technique out there in the world. That is just a base level but if one really wants to impact something, if one really wants to grow, the thought process needs to change from technique-driven to solution-driven mindset.

SK: Great interesting session!  Thank you so much for chatting with Ivy. Thanks a lot for your time today.

Shromona Kahali- Content Strategist -Ivy Pro School

 

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