Covering Disruptive Technology Powering Business in The Digital Age

Over the next 10 years, Data Science is predicted to become what Forbes described as the “sexiest job of the 21^st century” with the demand for Data Scientists outstripping the supply. According to some researchers the average wage of a data scientist on a global basis will be in the region of $90,000 USD with those fortunate enough to lead teams of Data Scientists potentially earning in excess of $250,000 USD per year.

The reality is that the job of a Data Scientist is a fairly new job scope with many people being more than a little confused about just what being a data scientist involves.  Amongst other requirements and qualities, data science entails a combination of skill sets such as good mathematical prowess, knowledge on machine learning algorithms, Hadoop technologies and applied statistics to name a few.

We decided we wanted to dig further and find out more about what it takes to actually be a data scientist.  What better way to do that than to speak to the people themselves? We reached out to several Data Scientists currently working in Malaysia and despite their obvious heavy workloads we were pleasantly surprised at their willingness to tell us more about what they do. Thanks guys.

The Data Scientists we spoke to were,

Asif Mohammad Iqbal – Principal Data Scientist at DiGi Telecommunications

Navin Manaswi – Senior Data Scientist at Xchanging

Ken Wei Tan – Data Scientist at Nogle Limited

Reza Moohebat, Data Scientist at Iprice Group

We asked Asif Muhammad Iqbal, Principal Data Scientist at DiGi Telecommunications, what does one need specifically to qualify as a Data Scientist?  His answer was certainly an unexpected one. He sites curiosity, passion to learn new things and common sense as a necessity, while adding that ‘your university degree can help’ too. He says being strong in maths and a logical mind will make you very successful in this field.

Senior Data Scientists at Xchanging, Navin Manaswi, says that passion for number crunching was what drove him to become a data scientist. His background of computational and engineering science helped him enjoy data science from a mathematical and computational perspective. Plus a keen interest in learning new technologies, algorithms and business domains also had a hand in his decision.

Ken Wei Tan, Data Scientist at Nogle Limited, had a different approach to taking up his Data Scientist post. He studied statistics in university while interning as a data analyst. According to him, the former laid the foundations for understanding the techniques he uses for work today, and adds, “the latter taught me to appreciate the importance of programming. I am still at the start of my career as a data scientist, but I was led here by my knowledge of statistics and simple machine learning, as well as experience in working with large data sets”.

Reza Moohebat, Data Scientist at Iprice Group shared with us that he started his professional career as programmer before going to do his post graduate. Which is where he got familiar with AI and data mining. It was there that the interest was cultivated before he found an internship and later continued as a permanent staff.

Navin’s typical day generally revolves around solving business problems through proper data analysis, data modelling and machine learning.  He shares that understanding business problems, fetching data from databases, preparing the data, building the algorithms for one of the recommendation engines, predictive models, clustering models, associative data mining, forecasting models, sentiment analysis engine, document classifier and sharing the insights with senior management are his regular activities at work.

Whereas Asif’s day to day as a Principle Data Scientist involves hands on model building, strategic discussions as well as mentoring rising data scientists. His work in model building requires that he explore data and discuss with the user in order to define the problem statement accurately before building any model. He then consults with other data scientist colleagues to have a second opinion before building the model.

Resa’s own start-up company was where he delved his interests into. “My company is a start-up and I have been there since it was a toddler in e-commerce. At the beginning we were focusing on data gathering and data cleaning.” He adds that in small companies you need to have some basic knowledge and experience working with data while having programming knowledge is a must.

We posed the question asking if they see themselves as part of IT or part of business. The views came back varied.

As Ken deals directly with the projects, his angle is completely business related. Navin and Asif’s view are of a balance between the two where they need to be on the side of business as well as understanding the IT aspects. Navin goes on to say that the return on Investment (RoI) on data science is very high. Getting projects, quoting higher price per project and creating a business value becomes easier once you add a predictive and prescriptive component to business project. “Data driven decisions always makes you a superior business leader.”

Resa takes a much more philosophical stance saying a persons views will change depending on the growth of the company. “You will learn to look at the big picture like other managers” he adds.

In closing we asked what advice would they give to aspiring Data Scientists.

Ken advises to get into a big data career and join a company with a data science program would be ideal. Staying up-to-date with advancements in data science is paramount since here is where new ideas are regularly discovered and developed.

Navin says to take your time to learn everything you need to learn. Most important is the ability and interest to solve problems. If you have problem solving skills and curiosity, this domain of big data/data science is meant for you. “No matter what technical skills you have, you can start learning data science”.

“A degree is not necessary if you are a quick learner” shares Asif. “Most importantly is to sharpen your math and logical thinking power, know your business and keep adding technical skills.”

Resa says to keep yourself updated by being around other Data Scientists. It will not be easy however, but attending conferences will be very useful.

The world of Big Data is in truth, just at its infancy and the people we interviewed are by default amongst the pioneers in the profession of data science. They are the among first wave of people working in a role that did not exist when as children they were asked “What do you want to be when you grow up”. We felt privileged to have the chance to speak with these people. Our overriding impression? To be a successful data scientist you need to be open minded, tirelessly inquisitive, logical in problem solving and combine this with an ability to communicate well with others. If that sounds like you, then maybe a new career path just opened up for you!

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