Lego Coder

Why Being A Production-Level Coder Should Matter To Every Data Scientist

Question: Ever wondered what could possibly be in higher demand in 2018 than a Data Scientist?
Answer: One that can code


It’s true. It’s really true.


As the biggest hiring gold rush of the 21st century continues its procession, hiring managers are becoming increasingly conscious to the biggest challenges their business face. This is helped in part by the expectations from senior executives, whom after all the hype, are now ready to see financial returns.


Perhaps, as a result, we are now seeing a seismic shift towards businesses that want their Data Scientists to have an excellent coding pedigree and the ability to turn ideas into revenue.


Our recent Data Science Salary Report revealed that an average of 44% of Data Science professionals who responded, can code at production level. Data Scientists that are able to conduct research and productionise their proof of concepts can save on time and cost.


Some roles will require Data Scientists to have the ability to code at a modeling level to be able to prototype and test model accuracy on smaller data sets. In this instance, it’s usually down to someone else in the business (usually an Engineer) to turn the prototype into an application.


So why are companies now looking for Data Scientists who can do their own prototype and production?


For one, some things can get lost in translation when more than one mind is involved in the process. How the application looks at the end of the journey in the mind of the Data Scientist, may be completely different from that of the Engineer. This can be a good thing, but can also be bad.


Another reason can be the delay in the process, if an Engineer is working on more than one project at that time. A Data Scientist who can write their own production code, will have the ability to get their application built and out there in the quickest time.


One of the most popular reasons for hiring a Data Scientist who can do their own production work is cost. It’s a no-brainer as a business owner to hire one person to do the same job as two people. Less budget expenditure, which keeps the boardroom nice and happy.


Popular Data Science celebrities will rightfully exclaim on social media that one of the key ingredients of a great Data Scientist is strong coding skills. They’re absolutely correct. One would argue that someone who has mastered research and has pushed themselves to become a great coder would make them a better well-rounded Data Scientist, able to satisfy any requests and expectations.


At Big Cloud, we are seeing on a growing basis that this is becoming a hard requirement from recruiting businesses. Our advice to budding Data Scientists: if you can’t code – start learning. If it isn’t already a critical part of your job, it’s going to be.


Chris Pearson, Co-Founder



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