Getting into the data science field involves a lot of risks, especially for professionals who want to make a switch from other streams. But taking risks brings self-doubts and anxiety due to the fear of failure. On the other hand, it could bring massive success if one is determined to triumph and do whatever it takes with diligence. Undoubtedly, one will hit many roadblocks along the way, but it is all about finding workarounds to eventually thrive and make that final leap into the data science landscape.
To bring such successful career switch stories to you, Analytics India Magazine interviewed yet another influencer, Srivatsa Srinath, a chief data scientist at Almug Technologies Pvt Ltd, for our weekly column My Journey In Data Science. The adage “greater the risk, greater the reward” perfectly fits at Srinath, who has accomplished great heights by taking bold decisions throughout his journey towards data science.
How It All Began
Srinath is a metallurgical engineer who graduated from IIT Madras in 2001. Then he followed it up with a masters in engineering sciences at Pennsylvania State University. Eventually, he joined Intel Corporation, where Srinath worked for almost eight years. Although he was performing well at Intel, it felt hollow to Srinath.
Consequently, over the years, he has continuously tried various roles. From being a metallurgical engineer to MS in electronics and VLSI design at Intel, he then worked in technical marketing and ultimately became a data scientist. “Initially, I was following the crowd, but I had to discover the inner calling, which encouraged me to try new roles. Thus, I followed my instincts, and I am happy where I have ended up,” explained Srinath.
During his transition from one role to another, Srinath took several risks, and one of them was moving from Intel to work for a much smaller company, Ittiam Systems, in 2012. There he worked as technical marketing and also enrolled for PGSEM program at IIM Bengaluru a year later. However, he was still not content and took another unusual decision to re-evaluate his career by taking a break. Mid-way through the PGSEM program at IIM-B, Srinath understood about the statistics and its use cases. Therefore, he decided to place a bet on the emerging data science space. Srinath rigorously learned the data science skills and joined Netwala Inc after five months as a data science consultant.
Data Science Preparation Strategy
After figuring out the path, the next move as data science was for Srinath to enrol for advanced courses in mathematics at IIM-B, learn from online courses provided by Stanford and MIT. Unlike other education tech startup that offers two to three-month courses, he focused on lengthy courses by Stanford and MIT, which he says allowed him to obtain a solid foundation. However, he stressed on the fact that one can only learn the basics from courses, real learning happens by developing one’s interest and trying out several techniques to solve problems.
Talking about his struggle while gaining the knowledge of data science, Srinath said learning the ropes and mastering the techniques was difficult. Therefore, he devised an effective strategy. “Rather than focusing on the tools, I chose to focus on problem formulation and problem-solving, leading to optimal decision making. To do this, one needs to develop the intuition behind the algorithms rather than utilising techniques haphazardly.
Methodology To Get A Job
What makes Srinath unique is that instead of directly applying for data science jobs, he joined an early stage networking startup and within a month, he started adding value to the firm with his data science skills. “The key to success in a startup is to find a workaround to challenges that do not require intensive resources through data science practices,” explains Srinath.
Despite knowing the risk of working for a startup on his financial needs, Srinath was up for the challenge. To support his finance, he began teaching machine learning to corporates and at educational institutions. “In a month, I used to work with the startup for three weeks and spent a week delivering data science training, says Srinath. “Over three years, I clocked over 900 hours of training in data science. While the teaching helped me in achieving a solid fundamental understanding, working with startup empowered me to gain practical experience.” Such a methodology uniquely positioned him for the future job interview as his resume stood out from the rest.
How Does Srinath Stay Abreast In The Ever-Changing Data Science Landscape
Data science is a vast field, and organisations have higher expectations from data scientists, thereby, keeping oneself updated is critical to thriving in the competitive landscape. Thus, Srinath sets the bar high to achieve excellence by following the best in the field and seeking help from mentors to continually evaluate himself. This has assisted Srinath in charting out a career roadmap effectively, rather than going by buzzwords or cool new tools. “I listen to people who are making a difference in the data science landscape, continue to teach others till date,” describes Srinath.
Srinath also stressed on the importance of staying updated with the research that happens in the data science marketplace. Further, he pinpointed that perseverance is crucial as it helps in sharpening skill every day, which he believes is enough to be among the top in the industry.
Data Science Work Experience
Projects are one of the most crucial things to uniquely position oneself, and for Srinath, it is no different. He suggests that as a data scientist, one needs to be equally comfortable in handling all sizes of data, not just big data. Smaller data gives the flexibility to built more productive structures, which results in insightful and explainable models. Bayesian approaches and causal inference can be used to provide better business decision-making opportunities. However, with big data, deep learning technologies, like in computer vision, gives state-of-the-art results. Srinath has acquired such insights while working on diverse problem statements in computer vision, forecasting, and scoring solutions. “Most data scientists complain that their work doesn’t involve cutting edge techniques in their day to day work. However, I think they fail to understand how the methods should be used for different problems,” says Srinath.
Currently, as a chief data scientist, Srinath manages a team of data scientists, who work on forecasting problems and scoring solutions. Besides, he hires fresher data scientists, where he seeks candidates who are hungry to learn new concepts while still being strong in the foundational concepts. And for experienced data scientists, he looks for humility and an inclination to share one’s knowledge as spreading information raises one’s bar and allows others to grow.
Advice To Aspirants
Srinath advises aspiring data scientists to solve significant problems rather than going behind companies and the titles. Besides, he pinpoints the requirement for developing a sense of humility in what one knows and self-confidence that he/she can learn anything with effort. He concluded by saying that apart from learning tools and frameworks, one should have an eye on the latest trends, shortcomings, and the potential challenges in the landscape.
