Earlier this month I had the pleasure of speaking at ODSC East
, as the best future talent Data Science talent gathered together to discuss the direction of our industry.
With Data Science becoming such a broad term that covers a number of functions, and with the rise of new areas such as Blockchain, AI and ML, I wanted to talk about what it actually means to be a Data Scientist now, and in the future.
With this in mind, we conducted a survey over the course of the event where we asked what Data Science meant to the people there. Here’s what we found out:
WHAT IS DATA SCIENTIST, ACTUALLY?
Every company thinks they need one, and every analyst wants to be one, but more and more job titles that are not necessarily Data Science are now being billed as Data Scientists. In fact, when we asked people what they considered their job title to be, regardless of experience, Data Science came out on top:
- Data Scientist: 58%
- Data Analyst: 22%
- Machine Learning Engineer: 10%
- Business Intelligence Analyst: 9%
However, from my experience, this is not necessarily accurate. I once worked with the Senior Manager of Data Science in a very well established Retailer. He’d been there for less than one year and was already on the job market. In his interview he had been told that the company were fully behind investing in a top-class Data Science department but had actually ended up managing a team of people who were building dashboards creating reports for all areas of the business. This is much less Data Science, and much more Business Intelligence.
This confusion is quite typical within the industry and frequently needs to both unhappy employers and employees.
MORE THAN JUST TOOLS
One common mistake when it comes to misidentifying Data Scientists is a result of people focusing on the tools people use. Whilst both Data Scientists and Marketing & Insight specialists might be skilled up in Python, R and SQL, their methodologies are significantly different. When asked to define a true Data Scientist at the event, 73% of people agreed the definition is:
“A person who uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.”
Companies who panic about needing a Data Scientist to keep up with their competitors often ignore these crucial points and end up listing every tool on a job spec. Frequently those who claim they want a Data Scientist actually want an Insight Analyst who can understand how customers behave, what they respond well to, what they’re talking about on social media, and how this unstructured data can be used to help their business make better decisions.
WHAT DOES THIS MEAN FOR ME?
For someone wanting to work in the Data & Analytics field there is one key rule: Know Yourself.
Think carefully about aspects within your Science, Operational Research, Statistics, and Analytics in general that you enjoy and how you can work them into your career. If you’re in college and just starting your career, don’t limit yourself by the sectors you think you have to work in; enjoy gaming? The gaming industry uses Data to make characters more lifelike, make sure they move in real-time and ensure that they play in a realistic way.
Just as crucial, however, is having an understanding of what the analytical teams around you do. Consider what roles they play in your business and how you are all interlinked, whilst being aware of the unique differences between roles. And, outside of analytics, those who understand what impact their work has on a business will always stand out amongst a crowd.
Essentially, don’t let yourself be limited by the title of Data Scientist. There are hundreds of roles within Data & Analytics so think about which one is right for you, rather than following the crowd.
If you’re looking for your next opportunity in Data & Analytics, or are looking to build out a team, take a look at our latest roles
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