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Evolution of the Data Scientist: How Number Crunching Became the Number One Job in America

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Mark Simon HeadshotIn this special guest feature, Mark Simon, Managing Director of Toluna North America , tracks the history of the “data scientist” job description and how its importance to today’s enterprise has increased in a number of important ways. Before joining Toluna as a founding member of the UK business in 2005 and later serving as Managing Director of the UK office, Mark worked for Greenfield Online, a pioneer in internet research acquired by Toluna in 2009. Prior to joining the internet research revolution, he worked in data planning and direct marketing. Mark has a degree in Business Economics and French from Southampton University.

Glassdoor recently released an independent research report stating that “data scientist” is the number one job in America.  As someone who has worked with data and analytics at Toluna for years, this news caused me to reflect on what we do and why it is so important for businesses like ours to engage employees with this kind of expertise.

The nature of the job has changed substantially since I started working in the market research industry. Clients now have access to so much non-survey data that can be used to extract insight. When interpreted correctly, these new sources can augment or replace marketing research programs. Examples of such non-survey data include social media, passive measurement, NPS, sales data and more. Large retailers and other organizations rely increasingly on the sources listed above when making important business decisions.

Brand owners need partners who can assimilate these disparate data sources to provide compelling narrative and a path to action. While many people may think of data scientists as number crunchers sitting in a cubicle, the modern data analytics expert is so much more.

Uncertainty in the Beginning

When the profession emerged, companies knew they needed marketing experts and statisticians, but there wasn’t an emphasis on an ability to glean actionable insights from that data and communicate directly with clients.

The role demands that market research professionals not only demonstrate experience working with data and crunching numbers, but also that they can tell a story about that data. I look for an intersection of three key qualities: statistical expertise, some degree of coding or technical understanding and communication skills. I want colleagues who can think quickly on their feet, who can talk to a client and frame their findings effectively, and who aren’t afraid to make a decision and stick by it. The job isn’t just about crunching numbers and making graphs, it’s about finding meaningful patterns and presenting your findings effectively.

Shifting Job Requirements

This shift in what it means to be a data scientist is not unique to Toluna. Further, data scientists are now in more client-facing roles. This direct flow of communication leads to improved results and understanding. This means that we can’t look at our data analytics experts as solely mathematicians or computer scientists. Having the ability to harness, interpret and make decisions with the most up-to-date business intelligence is what separates companies who are leveraging data in the most efficient way, from those who are not.

The Big Data Factor

The rise of data has had an undeniable effect on the industry. Most companies have embraced the idea that the more data they have, the more valuable the resulting information will be. While it’s true that large data sets can lead to better insights, it takes a sophisticated strategy to cut through the noise. The sheer size of these data sets has created demand for people that can find patterns and meaning in the numbers to distill information.

Without a deep understanding of marketing interests and a direct connection with the executive seeking answers, producing actionable insights becomes extremely difficult. Further, what once took hours is now expected to take minutes. Companies are starting to realize that the only way big data analytics can be  used efficiently is if the people digging through the data sets know exactly what their clients are looking for and how to find it.

The Data Scientist of Today

The new data scientist must be an intuitive, analytical, client-facing and technology-wielding individual. Any less and your market research strategy will pay the price. The exciting news is that there are so many people out there who are passionate about this field and thrilled by the challenges it presents. It is these new demands that have pushed data scientist to the top of the job market.

 

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