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Meet Dr Santanu Sinha, A Data Scientist Who Says That Life Is Bigger Than A Few NOs From Someone

Meet Dr Santanu Sinha, A Data Scientist Who Says That Life Is Bigger Than A Few NOs From Someone


To understand what it takes to become a data science professional, we are reaching out to experienced data scientists every week from the industry. For this week’s “My Journey In Data Science” we talked to Dr Santanu Sinha, Senior Data Scientist at Hewlett Packard Enterprise.

Sinha has over 11 years of experience in designing and developing implementable solutions for complex business problems across the industry. Before Hewlett Packard Enterprise, he has worked with companies like Accenture and TCS. 



Sinha is a mechanical engineering graduate from NIT Durgapur and has a PhD in Operations Research and its applications in Supply-Chain from IIT Kharagpur. Despite the fact that Sinha is from the mechanical domain, he had a fascination towards data science because he believed that it is a highly interdisciplinary study and there is a lot of flexibility.

“Unlike other areas, data science is more fact-based and hence, more practical. Further, it is application-oriented. So, you can see the effect in real-life and play it better to make it more useful,” said Sinha.

When asked about his views on data science scenario at present,  Sinha said that the field is growing at a terrific speed, there are a lot of opportunities to contribute to this field and at the same time, one can learn a lot.

The First Job Story

Irrespective of the industry, the first job is always a memorable one for every professional and Sinha is no exception. When asked how he landed his first job, he said that in terms of job hunting, he did not have to struggle much as it was a campus interview. However, he had to put in an immense amount of effort to prepare for the interview. “I studied extensively about the company, their business, a few problem areas in the industry – apart from the subjects those might be of direct importance to the hiring managers,” said Sinha.

He also shared his experience with the interview process. He had to appear a few rounds with hiring managers, senior peers, stakeholders, and senior-managers including a presentation on his research areas. “I still remember the board was full of smart and incredibly inquisitive people and I had a tough time to answer a couple of questions. However, each session was quite positive, and I learnt a lot from each of them,” Sinha added.

Learning Phase

To become a data science professional, one has to have immense knowledge. According to Sinha, the basic building blocks of data science are mathematics, statistics, simulation, optimisation, programming, theories of computer science, and domain expertise. And he has made sure that his knowledge and expertise in these subjects is high enough. “Having a quantitative background, my journey was a bit easier for me. Also, I am passionate about programming and till date I enjoy it,” said Sinha.

Talking about the next phase of learning, the senior data scientist said that domain knowledge usually comes with work experience. He also said that there may be a need to seek external support if one cannot self-manage an area. However, according to him, the source does not matter. He believes what matters is a focused learning objective with an honest self-evaluation, assessment and priority.

Sinha’s Take On Handling Setbacks And Thrive

Talking about setbacks and rejection, Sinha believes that everybody is programmed to handle success. However, it is equally important to learn how to handle failure as well. “A rejection is just an indicator of many things, for example, profile mismatch, lack of expertise, missing that ‘X’ factor, and so on. Take it as great feedback and work back to be in shape next time. After all, life is bigger than a few NOs from someone,” said Sinha.   

And when it comes to thriving in this domain, it is imperative for a data science enthusiast to keep the knowledge gaining game-high. To understand this better, we asked him whether he ever felt that need to learn more stuff or others know more than him. He said that this happens quite often to him. “Data science is an ever-expanding area with significant transformations each day. Each one of us has a lot to contribute,” said Sinha.

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And that is why he believes that in a project, there is more team-work and ideas and individual work. It’s all about appreciating each other and learn and apply it on the next occasion — the best solution can come from anybody from the team.

When asked about his strategy on how to thrive, the senior data scientist said that one of the most important strategies is to be relevant in business through continuous learning, Creating IPR, creating visibility through teaching or mentoring, and enjoying what you do. Do an honest evaluation if you really want that job or that role. Do not force-fit anything. Then everything will fall in place,” Sinha added.

The Best And Worst Experience

We also asked him about his most successful project as a data scientist and he said that any project that gets implemented in real-life business with a proven business benefits thrills him. At the same time, it inspires him to make it even better – either for the next project or the same one with continuous improvement.

However, there is one incident that remembers, and he shared with us. In one of his initial project, he and the team were working on optimizing a supply-chain network for a global transporter. But, in the code, instead of ‘Cost Minimization’, carelessly, they ended up programming a ‘Cost Maximization’ model. And the results were all absurd. Instead of a tight network, it was showing a far-fetched one. “We looked at the whole scripts – still could not find the bug. Then suddenly, one discovered the typos in our Java code! Learnt a great lesson that day,” said Sinha.

A Piece Of Advice For Aspiring Data Scientists

Data science is a Knowledge-intensive industry and to prosper and grow in this domain, one must keep learning as much as possible. “Learning can happen in multiple ways — certifications, self-read, peer or group learning, internship, cross-team projects, and so on. Document all your learnings and try to apply them into new contexts,” Sinha said in conclusion. 


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