The Changing Role of a Data Scientist

Liz Petoskey

September 4, 2019

The most prestigious and sought-after careers on the planet right now include the likes of Data Scientists, Business Analysts and Software Engineers.

Last year, Bloomberg referred to data science as ‘America’s Hottest Job’ and they weren’t wrong. This year, they revealed the highest paying jobs for new graduates and ‘Data Scientist’ is top of the list.

So, why has the relatively young role of ‘Data Scientist’ soared in popularity, and why are companies keen to recruit for this esteemed position?


The Desire for Data

Data has quickly become the world’s biggest commodity. It has been likened to oil by numerous media outlets, claiming that data is the ‘oil of the digital era.’

As the data economy thrives, more and more firms have begun to realize the value and importance of data for innovation, competition and ultimately increased revenues. This is the primary reason why many firms are investing heavily in artificial intelligence and machine learning – to collate and leverage large amounts of data. To do this successfully, they require the knowledge and expertise of Data Scientists to ensure they maximize data assets and capitalize on them.



The title Data Scientist hasn’t been around for that long, and some say the role is the coalescence of statisticians and the field of computer science. It can also be argued that, before Data Scientists we had Research Analysts, Data Analysts and Research Scientists.

Nowadays job titles change rapidly, partly due to innovation and the emergence of new careers, but also due to culture and the competition for talent.

At its core, the role of a Data Scientist is to identify problems, opportunities and questions, and seek answers by cleaning, analyzing and interpreting large amounts of data. However, things are beginning to change, and many believe that the role as we know it today will be barely recognizable in 5 to 10 years.



Over the last few years data science has been at the forefront of changes in education. Universities and other academic institutions have restructured their programs to cater to the growing demand for data analytics and data science skill sets. The increase in academic courses, coupled with the media hype, have elevated the role to new heights and led to an influx of fresh graduates seeking careers as Data Scientists.

However, the goalposts have moved. Some companies are now looking for domain specialists with a demonstrated history and years of experience.

What’s more, due to the role being branded in the media as ‘The Sexiest Job of the 21st Century,’ we often see a mismatch in the job market. Some companies jump on the data science bandwagon to entice top talent, when in reality they’re looking for someone with a different set of skills. To some extent this creates a skewed perspective of what a Data Scientist does and how many opportunities there are available to candidates.

Similarly, there can be a mismatch between job descriptions and the reality of what that job entails. In some cases, the requirements far outweigh the day-to-day responsibilities and leave many worthy candidates unable to apply because their skills and experience do not meet the prerequisites.


 Needless to say, data science is playing an increasingly important role in organizations and is of board-level priority. As such we’re going to see more in-house teams being formed that will have a strong and positive influence across other departments within an organization.

As that emphasis grows people will become specialists in certain areas of data science such as data mining, preparation and interpreting. The role will begin to fork off into different niches and people will develop domain knowledge of their industries and the application of data science within those industries. As this specialization occurs, we could begin to see new roles emerge such as fraud data scientist, risk data scientist and cyber security data scientist.

As for the nature of the job itself, there will be an increased focus on problem solving, driving growth and the ability to put models into production. As expectations increase, deep knowledge of AI and ML is no longer a nice-to-have for Data Scientists but a must-have skill.

The availability of tools and software that assist, and in some cases automate, data science tasks has helped to steer the direction of the role. There will also be more turnkey solutions available to small- and medium-sized businesses which will enable people and organizations who do not specialize in machine learning or statistics to leverage user-friendly tools and generate advanced outcomes.

Some believe that a basic understanding of data science will become a required skill for many jobs in the future.


So, what does it take to be a Data Scientist of the future?

A successful Data Scientist is multidisciplinary and possesses a holistic skill set comprising not only technical skills but also soft skills such as communication, creativity and teamwork. To thrive in the competitive job market, he or she will need data engineering skills and the ability to build pipelines that deliver data to platforms where analytics and visualization take place.

An exemplary Data Scientist will add value by asking the right questions and knowing which data sets to use to find the answer. He or she will also have a passion to apply their knowledge and skills to problems that will benefit a wide range of people and have a positive social impact well beyond the workplace.


Does this sound like you?

Well, you may be in luck, as we’re currently recruiting Data Scientists to join our growing team in Chicago! Click here for vacancy details: