Data Scientist – HP
Over 5 years of experience in the field of data science
Previous experiences – TCS and IBM
Numerical programming, mainframe database programming and Business Intelligence.
Excerpts from Anish’s interview with Manipal ProLearn where he talks about his role as a data scientist:
Manipal ProLearn: What attracted you towards data science, a field quite unheard of when you started off?
Anish: I began my career in 2001 as a Programmer for a Reputed Card Processing client, where I handled a Credit Card Fraud Detection project, which can be called analytics today. So I will say I have been handling “Data Science” since 2001, though that term was not coined then. I also worked as a Database programmer all throughout my IT career so handling data was not new to me.
Manipal ProLearn: Did you study any data science-related subjects in NIT?
Anish: There was no separate curriculum for data science those days. There was Computer Science and I studied Mechanical Engineering. During my undergrad days at NIT, I worked on a genetic algorithm-based Fuzzy Logic Program for Medical Diagnostics – something which would be “cutting edge” analytics in today’s parlance. Those days it was called smart computing. However, data science as an established field was not developed then.
Manipal ProLearn: Since you are an MS and Ph.D. in M.E, do you think you have an edge over other data scientists?
Anish: Definitely. Having a Ph. D. and a research-based Master’s degree helps me look at things beyond a developer’s perspective. A lot of work of current data scientists involves building medium to complex mathematical models to analyse data rather than just writing programs. It is said that mechanical engineers can go into any field! Jokes apart, the sheer diversity of mechanical engineering allows graduates to choose paths, which are more aligned with their interest.
Manipal ProLearn: Any data scientist whose work you admire?
Anish: Geoffrey Hinton (Google), Yann LeCun (Facebook AI Research), Yoshua Bengio (University of Montreal) are god-fathers of deep-learning. Andrew Ng (Baidu) is a visionary teacher and industry leader. Again, there are many more data scientists who are doing a commendable job in the field, and I have noticed that most of them come from companies like Google, Amazon and Facebook.
Manipal ProLearn: The most important thing you have learned so far as a Data Scientist.
Anish: That data does not lie. Any anomalous behavior must be tracked to its origin as it may lead to new investigative paths. Even if a single row of data behaves differently from 99 or 1000 other rows, you should spend time to track that one row. It may lead to something valuable.
Manipal ProLearn: What do you enjoy doing in your free time? Your hobbies?
Anish: Cooking is a stress reliever for me, I like to explore different cuisines and often mix and match traditional recipes. I got into the habit of cooking during my college days, while staying at a hostel. Another hobby is solving puzzles, which actually helps in having a sharp analytical mind, which is important for data scientists.
Manipal ProLearn: What according to you are the most vital skills/qualities which every data scientist should possess?
Anish: I think a strong foundation in mathematics or statistics with a good problem-solving aptitude is a must. Having a solid foundation in programming is also necessary. An added advantage would be having some knowledge about databases.
Manipal ProLearn: What has been the proudest moment in your career so far?
Anish: Winning the Research Excellence award during my Ph. D. tenure at IISc was a memorable moment. Being awarded the Best Research presentation award twice during my Masters Research at Louisiana State University was another proud moment.
However, the fact that my Ph. D. research thesis culminated into a commercial Engineering software takes the cake. Knowing that your hard labor has gone into making a tangible entity and is not just lying in an abstract form in research publications makes you feel proud.
Manipal ProLearn: A song that you like to sing/hum while number crunching?
Anish: I do like listening to music, but I normally do not hum or sing while working. While working I focus on it completely and prefer to listen to music when I want to relax.
Manipal ProLearn: What as per you is the most challenging part of being a data scientist? Any suggestions on how to overcome it?
Anish: I feel that the most challenging part is to provide a simple solution to a complex problem. It’s always easy to come up with a complex algorithm for a complex problem. However, it’s challenging to come up with a feasible, but simple solution.
Manipal ProLearn: Any book or movie that motivates you every time you need some motivation at work?
Anish: The Sylvester Stallone-starrer Rocky series is very motivating. These movies depict his real-life struggle and how all his hard work has made him what he is today. I find that inspiring on a personal level too.
Manipal ProLearn: Three words that describe your working style.
Anish: Methodical, meticulous and innovative. I believe that normal is boring, so I always try to find new ways of dealing with problems.
Manipal ProLearn: Your advice to young data scientists or those interested in this field.
Anish: I would like to tell them that there are no shortcuts to success! If you wish to take up any data science specialization, study thoroughly and build a solid foundation first. I believe that when you take up a course, it may only be like an icing on a cake, but you have to bake the cake on your own! You need to have the zeal and passion to learn and excel.
Data science is all about learning – learning new insights, learning the best representations etc. So an essential skill is to learn continuously, and perpetually. The field is changing fast, and it is imperative that new knowledge is assimilated at a faster rate. Make use of all the available resources and learn on a daily basis – from YouTube, blogs, hackathons, beyond what is being taught in the university curriculum.
Manipal ProLearn is thankful to Anish Roychowdury for taking out time and giving us valuable insights in the field of data science.