Academics In Data Science

4 Reasons Why You Need to Hire an Academic for Data Science

The Propheto Team

Article published: January 13, 2021


There is a broad misconception that academia and industry are worlds apart and that the academic’s skillset is not transferable to industry problems. This narrative is far from true, particularly within STEM (science, technology, engineering, math) and computational fields. Within programs like physics, bioinformatics, and statistics is a vast pool of individuals using a combination of hypothesis driven exploration, cutting-edge data science and analytics techniques, and illustrative storytelling to find answers to the world’s most challenging questions. These skills are highly transferable and valuable for companies as they think about how to retain their customers in a given year or how to make a product intelligently provide recommendations to users. Academics are some of the most qualified individuals in data science and analytics.

In this article, we demonstrate why academics make strong candidates for data science.

1. Academics Are the Best Problem Solvers

Academics are Problem Solvers

Academics are solving the world’s most challenging problems. Researchers at Indiana University Bloomington are using data science techniques to predict fake news while researchers at Harvard are studying the broad implications of climate change by examining global temperatures and crop yields. Exploring and developing novel insights for such large-scale problems eclipses the complexity of day-to-day business analytics challenges, such as personalization and customer retention. To create a competitive edge with cutting-edge insights, this level of analytical rigor is a prerequisite. With their extensive training, academics are well equipped to handle the challenges in industry.

2. Academics Are Skilled Communicators of Complex Ideas

Academics are communicators

Making a breakthrough discovery is only half of the work in academia. The other half is communicating these findings in a clear and concise manner. Through research reports, presentations at conferences, and teaching in classrooms, academics are accustomed to converting complex ideas into a message that is easy to consume. In industry, the ability to influence and persuade using data has increasingly become the norm. Managers make decisions not solely based on data but on how that data is presented. From their experience in research on far more challenging problems, academics are highly capable influencers within organizations seeking to make data-driven decisions.

3. Academics are Masters of the Scientific Method

Academics use scientific method

Data science, like any hard science, requires a rigorous scientific method to explore and understand data. Data scientists must know how to ask and answer questions through a process of formulating and testing hypotheses. For example, when trying to examine what are leading indicators of churn, data scientists may consider a customer’s industry, title, software stack, or channel of engagement. Data scientists then run tests on the data to see whether or not there is a relationship between these factors and make adjustments based on the results. This ability is engrained in an academic’s DNA. Academics are better equipped than most to not only ask the right questions, but also apply the right statistical models to draw the most accurate conclusions and insights.

4. Academics Are Team Players

Academics are team players

Data science isn’t just one quant in a room crunching numbers. Data science is a team effort with a combination of data engineers, analysts, data scientists, and managers collaborating to develop hypotheses, set up the data, build the models, interpret the results, and determine a proper course of action. While it may not be obvious, working cooperatively is commonplace in academia. Multiple authors will collaborate, share data and results, and contribute to publish papers in academic journals. In academia, multiple researchers work together on different sections of a research project to bring their own insights that add to the completion of the research project.

So for companies struggling to find data science talent, look to academia. Masters, PhD, and postdocs have all the skills and training to meet the analytical demands to help companies unlock insights and value from their data.