Shubo Chakrabarti
Shubo Chakrabarti earned his MSc in Neuroscience from the Kings College in London and his PhD from the Penn State University. Shubo worked as a senior neuroscientist and project leader for several years at the Universities of Göttingen and Tübingen in Germany before switching careers to work for MathWorks, the makers of MATLAB – a technical computing environment used widely by scientists and engineers. In his current role, he works with Science Gateways and online research portals in EMEA to enable access to MATLAB on these platforms. Shubo is based out of Stuttgart in Germany.
Can you describe your academic and professional background? What path led you to pursue this field?
I am a neuroscientist by training. I started off with a Bachelors degree from the Presidency College in Calcutta, India, about 20 years ago. After graduating I decided further studies in neuroscience would be the right course. A generously funded scholarship enabled me to get my Masters in Neuroscience from the Kings College in London. I loved my first taste of research as part of my Masters Project, so my next step was a PhD from the Penn State University in Hershey.
My PhD work was based on recording from anesthetized rats and I realized that studying the brains in awake, behaving animals was crucial as anesthesia has strong effects on brain physiology. I found a lab at the German Primate Center in Göttingen who were studying reaching movements in primates. Instead of joining an existing project, I wrote my own grant which was funded by the prestigious Alexander von Humboldt Foundation. This gave me a lot of flexibility and independence early on in my postdoctoral career.
After my stint in a primate lab I ended up with my own workgroup in Tübingen in south-western Germany. My primary research focused on sensory perception and how this is influenced by pre-existing expectations within the brain. Running my own projects also taught me non-academic skills such as project management and the power of networking as I built up international co-operations with partners in the US and Japan. 13 years after completing my PhD, I realized I wanted to broaden my professional life beyond academia, so I switched careers to start working for MathWorks where I manage engagements with Science Gateways and research portals across Europe, the Middle East and Africa (EMEA).
How did you find this particular position, and what was the hiring process like? Is there a typical structure for this in your field?
I was using the MATLAB programming environment for analyzing my data. I was also teaching graduate students (mainly from the life sciences) to program their own analyses using MATLAB. At a neuroscience conference I saw MathWorks, the company that produced MATLAB, had a booth - so I went up to them and asked for suggestions for improving my course. That started off a long relationship between the company and me, where I incorporated ideas from the engineers at MathWorks to improve my course, and they got interested in how I was using their products for my research. I had come to regard some of the MathWorks engineers I interacted with as technical colleagues, and when one of them told me about this opening, I applied.
The interview process was very straightforward. I first spoke to the hiring manager in detail about the job so that I had a good idea about what it entailed. I was then invited to an on-site interview which consisted of me giving a talk about my projects, followed by 1-on-1 interviews with several people in the organization. What I really liked about the process, was that it felt more like a conversation than an interview and that I was kept informed at every stage by the recruitment office regarding the steps involved. It was very transparent. I don’t know if this is standard in the field, but I suspect it had something to do with MathWorks being a company of engineers and scientists who are very data driven.
Can you tell us about your current responsibilities? What is a typical day or week like in your role?
As data gets bigger and more scientists start using supercomputers for number crunching, an increasing number are turning to online platforms called Science Gateways for their computational and data needs instead of their local workstations. In short, Science Gateways are platforms where scientists and engineers exchange resources (data, computing resources and algorithms). My role at MathWorks is to engage with Science Gateways in the EMEA region and enable access to MATLAB on these platforms.
One of the great aspects of my job is the diversity of roles in which I find myself. I do a bit of everything. I talk to Science Gateway users (mainly scientists) to find out about their research workflows and their needs. I reach out to Science Gateway owners to discuss how our offerings can be best integrated onto their platforms. No two Science Gateways are the same, so there is no one-fits-all solution that would best serve them and their users. I am also involved in a lot of strategy meetings within the company, where I work together with colleagues in development and marketing to identify areas where we can optimize the users’ experience when using our products on Science Gateways.
What do you enjoy about your current job and work environment?
There are two aspects that spring to mind which I really enjoy.
First, the work environment. MathWorks software is not just used in neuroscience research but has a wide range of applications from aeronautics to finance. That means my colleagues have great domain knowledge about a very diverse landscape of industries and the opportunity to learn about the unique needs of each of these from them has broadened my horizons incredibly. This is what prompted me to leave academia initially – the desire to know how things are done outside my field and I am really enjoying that experience.
The second aspect has to do with my job. One contribution of Science Gateways is that they help the whole scientific process become more transparent – data must be organized according to specific standards and methods, and algorithms documented and published for reuse by others. So essentially by working together with these platforms, my efforts help in broadening access to scientific knowledge for all – Open Science, as it is known. I think that’s what science should be about.
What are some of the challenging aspects of your job? Is there anything you wish you had known about your job or industry before joining?
There are a lot of things you don’t realize about the world when you concentrate on your specific problem. In my research, I used MATLAB extensively. But there was a lot about the software I didn’t know. How development works. Product life cycles. Pricing. Software authentication and agreements. Understanding and appreciating a lot of these new concepts is challenging, but that’s what the on-boarding process in a company is about. I would really encourage colleagues stepping out of academia to take the on-boarding process seriously and use it to ask questions.
Do you have any professional plans for the future? What are some future career paths that could open up for someone in your position, 5-10 years down the road?
Research today has a lot more to do with infrastructure than it did when I started my PhD. Setting up effective and efficient data management and analysis workflows is almost as important as the research itself, because it ensures the optimal usage of the data. I would like to develop my knowledge about these infrastructures and processes as I think they will become even more crucial in the next few years. I think excellent domain knowledge in data management and analytics would be a great strength to have.
What’s changing in your industry? Are there any future trends we should be aware of?
Artificial Intelligence (AI) is going to be a game changer for the next generation just like the internet was for ours. The basic concepts behind AI are not new. The perceptron has been around since the late 1950’s and neuroscientists have actually used concepts of machine learning to try and understand how the brain makes sense of a very complex world. But processing capacity has changed since the late 50’s. We can store and analyze huge quantities of data now, sometimes using relatively inexpensive technology. So, AI applications will be seen everywhere – from predicting crop failures, to diagnosing diseases using symptoms, to recognizing patterns in text. The COVID-19 crisis has shown us how governments are relying on data science and modeling to guide policy. I think there will be a lot more of that in the future.
What activities, internships, or organizations would you recommend someone get involved with to help them break into this field?
The great thing about scientific computing is that it is possible to educate oneself without the need for expensive machines. An internship in a startup developing an app, for example, can be a great way to understand how software development and data analysis works. If you want to know more about how to extract meaningful information from data, start with a dataset regarding whatever interests you – US presidential elections, COVID-19 statistics, crime rates for your province or calories in food sources. Start asking yourself what you want to know and try to find the right statistics or methods which might help you uncover that information from the data. Hackathons are another great way to put your analytical skills to the test for a cause.
Is it common for people in your field to have a scientific/academic background (i.e. have PhDs)? Can you think of any advantages or disadvantages someone with a PhD might experience while pursuing or working in your field?
I think a PhD helps one with logical thinking and hypothesis driven reasoning, but I do not think it is a must. Many of my colleagues have a PhD, but many also don’t. Your graduate career might be a great time to pick up new techniques and increase your knowledge base though, as you have a lot of freedom to explore and experiment during your graduate studies.
Do you have any final words of advice for those navigating these career questions? Is there anything you would have done differently given what you know now?
I think success in every career looks different as it needs to optimally utilize your skill set, personality and opportunities. The key thing is not to take somebody else’s model and apply it to yourself but rather develop your own model that makes best use of your skills and desires. If you like making stuff, maybe you will be great in development but if interacting with people is what drives you, you might want to think of a customer facing role. You should also not let others decide what success or failure look like for you. A postdoc or a publication might be a measure of success for somebody else, but if that’s not getting you closer to your goals, it’s not true in your situation.
Keep yourself informed of your options. Visit career fairs, talk to people outside your research field, even outside your industry. The more options you know about, the more options you have. Have a chat with people in related industries. I once talked to a senior manager in a pharmaceutical company and asked him about what he looks for in potential candidates. He was very helpful and gave me some comments on how to improve my CV and bring out my skills better. Most people will give you invaluable advice, but you have to ask. LinkedIn and other professional platforms are a great arena to network and gather input.
Make sure your resume and your cover letter reflect all you want to say about yourself. Often that is the first representation of yourself that hiring managers will see. If there is something unique about you or your experiences that make you perfect for a job or career, make sure that comes across powerfully in your application.
Good luck!
