Springboard Mentor Spotlight: Dipanjan Sarkar

Meet Dipanjan Sarkar, a mentor for Springboard’s Data Science Career Track. 

A veteran data scientist with a passion for teaching, Dipanjan (“DJ”) Sarkar is a lead data scientist at Schaffhausen Institute of Technology (SIT) Academy in Switzerland, which runs technology bootcamps for professionals. 

Since becoming a mentor, he has discovered his love of sharing information with others and ensuring they don’t go through the same struggles he experienced when he first started without a mentor. Now, he’s mentored professionals of all levels, from recent college grads to C-level executives. He regularly speaks at conferences and webinars, and he’s a massive fan of MOOCs (Massive Open Online Courses) and even beta-tested a few Coursera courses before they went live on the site. 

DJ had a deep dislike of math until the ninth grade, when he learned statistics, linear algebra, and calculus—the three pillars of machine learning. Now, he’s a published author and was recognized as a Google Developer Expert in Machine Learning by Google in 2019. He was also recognized as one of the top ten data scientists in India in 2020, and ‘40 under 40 Data Scientists’ in 2021.

Tell me about your current role as lead data scientist at the SIT Academy.

I’ve been with the company for about two years. Our focus is helping people upskill in data science. We work with individuals and companies to help them build the necessary skills in data science, machine learning, artificial intelligence, and so on. We also do consulting projects with different companies to solve problems in data science using AI strategy and so on.

Do you work with a lot of people who come from non-technical backgrounds?

It’s a combination. Most of them have PhDs and a few years of experience in their field. A lot of them come from non-technical backgrounds like architecture, but most of them have some experience with technology. 

What do you like most about being a mentor at Springboard?

 I’ve been involved with Springboard since 2017. It has always been a passion of mine to share knowledge and help people in this field so that they don’t face the same roadblocks I faced when I was starting out. Mentoring has been very rewarding for me because I can give back to the community and put people on the right track by giving them the hand-holding they need to build a solid foundation in data science and jumpstart their careers.

The second thing is, it keeps my skills sharp. I even get to learn a thing or two from my mentees because they come from diverse backgrounds. 

What are some of the things you’ve learned from your mentees?

I’ve had people come from different domains, like healthcare, e-commerce, or government, and they’ve taught me how different organizations work, which enriches my domain knowledge.  

They tell me how they perceive things differently in their own domain. I’ve also worked with people who come from academic research backgrounds and the way they approach problems has helped me improve my own way of approaching things.

What is an unexpected quality you look for when you’re hiring a data scientist?

One of the main things I look for is: can they think outside of the box when a problem is presented to them? Book-learned knowledge or knowledge from a course isn’t enough; how innovative is their thinking? If a problem is challenging or ambiguous, can they put on their thinking cap and come up with an innovative solution? What I look for is an analytical mindset, problem-solving ability, and a never-give-up attitude.

How has being a mentor helped your own career development?

First of all, it has enabled me to work with people very easily because my approach to mentorship is very informal. I don’t expect to stick to a fixed agenda or only talk about the curriculum. If you ask any of my mentees, mentorship is about getting the guidance you need to support you in becoming a good data scientist at the end of the day. So the scope of our discussions is pretty open-ended. 

In that way, mentoring has made me better at communicating my thoughts. Now I speak at conferences, webinars, and other events, and a lot of people reach out to me. 

Data science is such a fast-moving field. How do you keep your skills sharp other than mentoring?

I have been actively networking since I started in this field, which means connecting with people who are doing the same thing as me, going to meetups or conferences whenever I can. Initially, I never presented anything. I just went as an attendee to hear about what other people are doing. 

The other thing I do is read articles and research papers online, watch videos and listen to podcasts.  

In an interview with AI Time Journal, you said that the data science job title is often misused. What are some of the misconceptions around it?

There’s a lot of confusion around what the role involves. Data scientists don’t just clean data. They use that clean data to answer questions, frame a hypothesis, and prove or disprove that hypothesis by building models. 

But now we have all these roles, like “data engineer,” which is someone who specializes in cleaning and processing data and building data pipelines. Sometimes job descriptions come with inaccurate job titles or they’ll list a bunch of skills that have nothing to do with data science, such as web development.

If your job is to create and update tables, then you’re a database administrator. Now of course, if you work for a small company, you may need to wear multiple hats. But I think the core aspect of data science is leveraging methodologies to extract insights from data. 

On that note, what’s your advice for those who are looking for their first data science job? What should they be wary of?

I always tell my mentees that when applying for a job, always check the roles and responsibilities in the job description. If you see a lot of things not related to analysis, extracting insights, or data modeling, then you should be careful. 

For example, the official title might be “data scientist” but in reality, they are looking for someone to write SQL queries day in, day out. If you’re writing SQL queries to solve business problems, you’re a data scientist. But if you’re writing queries to maintain a database, you’re just a glorified database administrator. There’s nothing wrong with being a database administrator, but when you look for your next job in data science and you’ve spent the last few years writing SQL queries to maintain a database, that doesn’t help you grow in your career. 

Do you prefer to work with companies that have a mature data science program and established processes or do you prefer organizations that are still a work-in-progress?

It depends on your appetite for risk. It’s like investing in the stock market. If you have a high-risk profile, you want to stay with the company long-term, and management is supportive of your work, the second option could work. In my previous role at Intel, I was one of the first hires on the data science team. I had great managers and peers and we built a team and it was an amazing experience. 

However, if you only want the job so you can solve problems, improve your skillset and domain knowledge, and then move on, you’re better off working with an established team because then you’ll be working on problems from day one.

What advice do you have for someone who’s just starting out as a Springboard mentor or is considering becoming a mentor?

Spend some time on the curriculum. Become very familiar with it, that way you can answer mentee questions effectively. The second thing is don’t go in with a fixed agenda. It’s okay to have a structure for your call but remember that every mentee learns at their own pace. Don’t push them too much, be compassionate, and be empathetic towards their learning process. If they get stuck, help them out, but don’t spoon-feed them the answers.

Finally, focus on having a good personal relationship with them because your job is to help someone grow. And it’s an amazing journey.

Since you’re here…
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