Breaking into Data Science: From Discovering the Higgs Boson to AI Consulting — Nitesh Soni
Learn about Nitesh’s career transition from academia to consulting.
As a Deloitte Consulting alumna myself, the diversity of talent that enters the world of consulting never fails to surprise me. I can draw a parallel to the even more proliferated world of Data Science today. In fact, career transitions into Data Science and AI are becoming more and more common with the screeching demand for Data Scientists, Machine Learning Engineers, Data Analysts and the like.
Nitesh Soni, Lead Data Scientist at Omnia AI — Deloitte Canada’s AI practice, is a superb example of talent that manoeuvred a transition from academia. During our initial conversations at the Analytics by Design (ABD) Inaugural Conference, we learned that Nitesh was a physics researcher in his former life and made direct contributions to discovering the Higgs boson. In contrast to the client-facing nature of his role in consulting today, Nitesh’s career change may seem challenging to some. We made it a mission to learn about his journey and to inspire those that might be contemplating a similar move.
Our correspondents, Aaron Zhang and Michelle Liu, sat with Nitesh for an interview late November, during which he touched upon the challenges that new entrants from academia may face when transitioning into the corporate world. He also elaborated on the ways academic research and practical data science diverge and intersect.
ABD Correspondents: Nitesh, thank you for taking the time to chat! To start, tell me about yourself and how you ended up in Data Science and AI Consulting.
Nitesh: Thank you for inviting me! Before I talk about my current role, I can share a little bit of my personal story.
I grew up in a small town in northern India. I always loved science and mathematics. Just like a typical science geek, I always strived for academic excellence throughout my education — like breaking the state record by scoring 100% in my undergrad math class! Fast forward to my PhD studies, I worked on the Belle Experiment at Laboratory KEK in Japan with a group of world-renowned researchers including some Nobel laureates.
Thereafter, I held multiple post-doctoral research positions at universities in UK, Canada, Australia and earned the opportunity to work at top ranked research laboratories like CERN in Switzerland and SLAC in the US. All of my research work was heavily focused on experiment design, data collection and analysis, which involved manipulating massive data sets and extracting rare insights from millions of data points. Naturally, I became immersed in the world of Data Science.
ABD Correspondents: Which research project stood out the most?
Nitesh: I would say the research that led to the discovery of Higgs boson — one of the biggest scientific discoveries of this century in particle physics. I made direct contributions as a part of the ATLAS collaboration at CERN.
ABD Correspondents: With already so much success in academia, what made you want to venture into the corporate world?
Nitesh: That’s a great question. I was relatively comfortable in research and had a lot of success — received a few prestigious awards including one from the Department of Science and Technology in India and had the opportunity to be part of the Lindau Nobel Laureate Meetings in Germany.
But after the discovery of Higgs boson, I was thinking that such discoveries rarely happen in quick succession. Though it is exciting to contribute in ground-breaking research, a discovery of this magnitude might not happen again within the tenure of my career in academia. This is when I started thinking about a path outside of research. I thought: let me challenge myself and venture into the corporate world where I can solve real world problems. Fortunately for me, the move worked well.
ABD Correspondents: What are the major differences between the nature of work in research and in the corporate environment?
Nitesh: The corporate world has many stakeholders. They need to focus on quick wins and short-term goals. So when selecting and implementing analytics projects, businesses may prioritize projects that realize business value quickly at the cost of reduced complexity.
Research roles require more in-depth knowledge of the subject at hand. Effort is invested in building or researching something that either does not exist or exists with unproven accuracy. In most cases, the impact of research work is not realized immediately or even in the near future. As an example, electrons were discovered in the 19th century and, at the time, no one would have thought that it was going to have such a huge impact on civilization until the 20th and 21st century. Essentially, every electronics gadget evolves around the mechanics of electrons in some way.
That being said, with more businesses starving for analytics talent, entry-level data scientists have a better chance of getting a corporate job more often than an academic one.
Lastly, there was one thing that definitely took me some time to get used to — the dress code! The most formal attire for me as a researcher back then was a blazer and jeans, and I would only wear them when attending important academic conferences. Whereas in consulting, suit-and-tie is the standard.
ABD Correspondents: What are the main challenges data scientists from strong academic backgrounds face when they enter the corporate world?
Nitesh: In the world of research, you are always surrounded by like-minded individuals who communicate in a similar language. However, in the corporate world, we interact with people from very diverse backgrounds and various levels of technical aptitude. I find that researchers often have a hard time delivering a concise message without getting into technical details. It is important for a data scientist to adjust their communication style according to the audience in the corporate setting.
The second challenge comes from the misalignment between personal interests and organizational goals. In a company, the outcomes of projects need to align directly with strategic objectives that are determined by the organization’s top leadership. As a researcher, I felt that I had more control over the projects I wanted to work on. To adjust to this misalignment, a data scientist from academia should learn to grasp the business context from the get-go and to apply analytical insights in a way that can influence the direction of the project.
ABD Correspondents: As an emerging field, there are many entrants looking to break into the industry. What’s your advice for those just starting out?
Nitesh: First of all, broaden your technical skills and immerse yourself in the world of Data Science. There are tons of information accessible online where virtually anyone can start learning. Though, technical aptitude is definitely important.
Secondly, build your network and let your curiosity guide you towards where you want to be! Attending industry workshops and seminars is a great way to develop a basic understanding of the field.
ABD Correspondents: Any specific advice for entrants transitioning from academia?
Nitesh: I think it is important to clarify the role you are looking for and to understand the types of positions that are suitable for you. There are many roles that do not require deep technical skills. For instance, if you are interested in project management, you likely will not need an advanced degree in machine learning, a certification in the subject area might suffice.
I often meet people that enter the industry without prior understanding of what the role entails. It is frightening and frustrating, not only for these individuals but also for the employer. We met a PhD candidate who applied for a consulting role and expected to do only hands-on coding, but in reality, a consultant needs to do much more than that. It is important to know what you want and to be transparent about your requirements with the employer. I think this mindset will help bridge the gap between the skills you have and the expectations of the company you want to apply for.
ABD Correspondents: All great points! Thanks again for taking the time to chat.
Nitesh: Of course — thank you for having me.
Omnia AI, Deloitte’s artificial intelligence practice leads all others in starting, enabling, accelerating, and sustaining the AI journey. Check out open roles at Omnia AI — Deloitte Canada’s AI practice!