Academia vs Industry

As I approach the final year of my PhD, the looming question grows nearer: Academia or Industry? For some, the answer is obvious, as their inclination has always directed them toward one or the other. But for me, the answer remains unclear, and it’s a question I’ve been seeking to answer.

It’s crystal clear to me that I wish to be a researcher. From a young age, I told my parents that I wanted to be an inventor. My younger self would conjure up ridiculous invention ideas, sometimes letting my imagination run wild with elaborate concepts that were hardly practical, and my passion for them would wane as new interests emerged. After several years of high school, the time came to choose a university course, and engineering seemed closest to my dream. Concepts like brain-controlled limbs and interactive assistive devices seemed to embody the future of engineering, and I aspired to be at the forefront of these innovations.

However, university proved to be a reality check for my fantasies. While I didn’t excel in robotics or mechanics, I found enjoyment in more software-related engineering projects, particularly in modeling, data processing, and data analytics. After gaining work experience in software development, returning to academia for research in data science, and participating in a data science industry placement, I’m convinced that research is the arena where I can fulfill my dream.

In many industries, research is predominantly associated with academia. The core of new scientific developments often originates from proof-of-concept projects initiated in academic settings. Recent examples include the CRISPR gene editing technology and the discovery of gravitational waves — both began as academic research endeavors before finding practical applications in various industries.

However, in the realm of data science, research projects often find their origins in industry and are subsequently validated or reproduced in academia. Examples such as recommendation systems by companies like Amazon and Netflix, as well as advancements in natural language processing by Apple’s Siri, Google Assistant, and Amazon’s Alexa, highlight this trend. Many academics in this field acknowledge that industry research often outpaces academic research, which makes academia appear less desirable from my perspective.

Over-expectation is a significant risk associated with industry-driven research, more so than in academia. Many research projects demand substantial investments of time and money, with the anticipation of novel outcomes. Companies often conduct research with the primary goal of generating profits from the applications of their findings. For instance, if a company invests in researching a new product, they expect the research to demonstrate the product’s success, enabling them to sell it for profit. This can introduce bias into the research process and impose tight deadlines that may compromise the research quality. Academia, on the other hand, doesn’t face these same pressures, though some departments struggle to strike a balance between numerous publications and a few high-impact ones, requiring careful management.

Contracts in academia tend to be shorter, offer lower salaries, and provide fewer bonus packages compared to the industry. This gap is particularly noticeable in the tech sector, one of today’s most prosperous industries. A data science researcher position at a prestigious UK university would more closely match the salaries of similarly experienced researchers in completely unrelated industries rather than those in industry research. Consequently, industry offers often come with salaries nearly double that of academia. Moreover, academic contracts frequently span 1 to 3 years, even for critical members of research groups, providing poor long-term stability. The longer contracts in industry also allow for better career planning and trajectory.

Research in academia promotes transparency and the sharing of knowledge with the broader community. This aspect becomes especially pertinent in the data science industry, which is currently embroiled in a transformative debate over whether research-related code should be openly accessible. Beyond this, the dissemination of learning through teaching is essential to the research spirit and lies at the heart of academia. As someone passionate about sharing knowledge, I aspire to work in a role that enables me to do so, reaching beyond just my immediate colleagues.

In summary, the decision to pursue a career in research within academia or industry is a challenging one, and it’s a decision I continue to grapple with. At the core of this choice, I must strike a balance between factors such as stability, interest, and passion. My hope is that I’ll eventually find a place that offers an ideal equilibrium of these considerations.