Luke Frost our consultant managing the role
Author: Luke Frost
Posting date: 6/19/2018 9:36 AM
The Fourth Industrial Age is booming. Data Scientists are rock stars of the tech world and data science is considered the "sexiest job" of the 21st century. But, when you take away all the buzz words and show, what does it all really mean? If you're just getting started in the field or know someone who wants to be, this is the first in a series of bite-sized articles looking at life as a Data Scientist.

Is Newton a hero of yours? Me too! Were science and maths your favourite subjects? Me too! As a Data Science specialist recruiter working across both research and commercial roles, I've had the pleasure to meet and learn from thousands of Data Scientists and other professionals within the analytics space, and here's what I’ve learned. 

What does a Data Scientist do?

A Data Scientist offers a holistic view of data with a clear understanding of how data comes together and the relationships between seemingly disconnected features. Below are three distinct areas where these manifest:

1. Experimentation 
2. Production 
3. Explanation

Testing, Testing, Hypothesise, Prove - Experimentation

As someone interested in Physics and Chemistry throughout school, the word science conjures up images of frogs being dissected, Newton being hit by an apple, and Bunsen Burners. Much like a chemist tests for chemical properties, playing with their experiments to define different results, a data scientist does the same - only with gigabytes upon gigabytes of data. 

The phase of experimentation for a Data Scientist is crucial, they test hypotheses, understand the limitations of algorithms and try to establish successful Proofs of Concept (PoC) to both prove and disprove their hypotheses. Once these experiments have proven success on limited data sets, then the process of production begins. It goes without saying that for experimentation to take place, there needs to be a clear structure to the data, an area that my colleague Josh Carter covers in his article Build IT and They Will Come. 

Putting it All Together - The Production Puzzle

When it comes to production, a Data Scientist has to juggle all aspects and implement a ‘clean’ solution that can run as efficiently as possible.

An isolated hypothesis is of little use to a business using analytics to shape policy and inform major business decisions. The complete dataset must be rolled out and continue to achieve similar results of the initial PoC to offer commercial impact. It must be able to work in harmony with all the other algorithms that are currently deployed. Once these initial PoC algorithms have been put into production and have produced an interesting output, there is one final stage to the process.

Tell Me in Plain Language - Explanation

Data Science has infused every industry, including retail. Much like a retail associate explains to prospective buyers the benefits and features of the product, so too must the Data Scientist be able to do the same. However, a Data Scientist must be able to break down a complex concept and be able to translate their findings into non-technical terms. 

This is an essential skill when you consider that very few commercial Data Scientists work in isolation, and in order for businesses to completely buy into Data Science, they first need to understand it.

As someone who's worked with Data Scientists and Data Analysts both in the retail industry and now, as a recruiter, I find this is one of the most fascinating parts of the process.I hope the above brief summary provides insight into a very topline overview of the way that a Data Scientists works within industry.

Please do take a look at our current vacancies or reach out to me directly. You can reach at or by calling me on 0208 408 6070.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

What’s Keeping Women Out Of Data Science?

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How Are Data & Analytics Professionals Mapping COVID Trends With Data?

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