It is a well-known fact that data scientist is among the most sought-after professions of the 21st century. With the abundance of raw data, which is only increasing by the day, companies are excessively looking for professionals capable of crunching numbers and extracting valuable information from them.
Of course, the perks of this job are second to none – you get a fancy title, excellent salary, and a chance to grow your career in one of the most advancing fields of our time. However, it is important to note that not everyone is equipped to meet these demands. In the US alone, there is a 50% gap between the supply and demand of data science professionals, with the demand set to increase further.
It is because most of the time, the applicants are not equipped to fulfill the criteria for these types of positions. Whether it is the lack of skills or domain knowledge, companies are not willing to compromise when it comes to these. With that in mind, we have prepared this quick, handy guide of 7 factors that companies consider when hiring a data scientist.
Proficiency
in Coding
As a data scientist, you will be handling a plethora of data that simply cannot be processed with excel. In such a scenario, you need to be able to work comfortably with software programs like Python and R – the two coding languages that have laid the foundation for modern data science.
Most of your day-to-day job may involve working with Python and its libraries, where you will handle large data sets, refine unstructured data, data processing & visualization, as well as putting it in a presentable format for further analysis. If you are in the analytical side of it, then you will be more geared towards using R for statistical models like data regression, cluster analysis, decision trees, et cetera.
Either way, having proficiency in one or the other, especially Python, given its versatility, will go a long way in making your case as a data scientist. After all, companies need people who can get work done efficiently. Have a few portfolios/pet projects in your resume, which depict your familiarity with the languages.
An
Aptitude for Math
If you don’t like Math, then you may be better off focusing on a different career field. Data-science is highly math-driven, with the core principles of statistics and probability being utilized every day to generate practical insights. Data scientists, by default, are required to process large amounts of data and leverage their math knowledge to generate statistical models that can shape key business strategies, as well as providing valuable inputs into its performance.
This often entails processing complex equations and breaking it down into simple, easy-to-understand conclusions. You ideally need to be good with linear algebra, calculus, and of course, stats and probability. Remember that over here, it is not the theoretical knowledge that is important, as much as the ability to use it practically. More than that, you should know what concept to use when.
There needs to be a clear link between what you need to process and model, and your ability to do the same using Python or R.
Domain
Knowledge
What good is becoming a data scientist if you do not understand the numbers that you are processing? At the end of the day, you need to know the why of it. You may be crunching numbers for a hedge fund every day, but if you don’t understand how it is impacting the business performance, the markets, or your clients, then it all becomes a moot point.
For this reason, a lot of companies pay importance to their potential data-scientists having thorough domain knowledge. This involves understanding the general trends of the industry, its performance in the past, the future outlook, market trends, the position of the company in the sector, its competition, along with other key aspects.
It is not just about numbers and coding, it is about making sense of it, and the only way to do so is by first understanding the bigger picture. Investment banking, finance, healthcare, insurance, whichever sector it is, you need to be familiar with it. Companies are likely to act on the insights on the assumption that all factors have been considered, including the domain trends we discussed above. Ultimately, the more you know, the better placed you will be to make something meaningful of the data.
Critical
Thinking and Problem Solving
This is not said enough, but the truth is told, this is among the most important factors companies consider when hiring a data scientist. In fact, the skill gap that we spoke about at the beginning of the article can be largely attributed to a lack of critical thinking among the applicants.
According to Omkar Raikar, founder and CTO of Business Toys Private Limited, as a data scientist, you should be able to identify the problems/questions from the raw data available in front of you, identify the most efficient way to process it and lastly, present the insights to the others in a neat, presentable manner. There may be 101 different ways to get some information from the data – it is up to you to figure out which ones will work the best, or rather, which ones will give you the most relevant information. This is where critical thinking kicks into play. You need to have the foresight on what to expect, as well as the ability to analyze the data objectively. In other words, you really need to use your head. No one is going to spoon-feed you – you need to be proactive on your end”.
Communication
and Visualization
As a data science professional, you will be working closely with different departments in your company to understand the data and the inherent problems better. This means there cannot be a gap in communicating the ideas and problems with others.
Similarly, the guys who are paying you the big bucks to process numbers do not care about how you arrived at your conclusions. All they care about is what can be done with it. You will be required to present your findings to the team, and the way you present it is of absolute importance. If you get too technical with the numbers, you will lose their interest; if you manage to give them practical insights and solutions, you will be their hero. It is not what data you have that matters; it is how you present it ultimately that will make or break your case.
Hence, having good communication skills is essential to thriving in the data science industry. You will be interacting with clients and your team members every day – you should do it right; otherwise, it may prove to be highly costly for the business.
Enthusiasm
for Machine Learning and AI
Machine learning and artificial intelligence are not just some fancy buzzwords – they are the core concepts towards which the data science industry is moving. Industries and companies alike are favoring ML and AI for their increased computing power and their ability to handle massive data-sets. You may start as a ‘regular’ data scientist, but as you progress, if you start showing a proclivity for machine learning, deep learning, and other advanced concepts, it will open up new career doors for you.
The sooner you get an understanding of these concepts, the better you can put them to good use for processing data. Of course, sometimes, it may not be needed at all – a simple Python code can do the trick, instead of diving deep into the chasms of AI. However, knowing which to use when is quite important.
Business
Oriented
Companies like it when their applicants are inclined to think from a business point of view. Ultimately, you are there to assist them, and if you are already a step ahead, it’s great for you. It just makes their job easier and eliminates any redundancies that may arise due to miscommunication.
Since your job is to process the data and present the insights to the company, it would be beneficial if you could tackle this approach from a business perspective. Put yourself in the shoes of the management and think about their expectations, their problems, and their outlook.
Parting
Thoughts
Data science is certainly the next big thing, and by the looks of it, it is here to stay. Given the massive gap in supply and demand, there is a lot of scope in this field to grow, but breaking in isn’t as easy as it seems. From having sound knowledge of maths and coding to having interpersonal skills, reasoning, and critical thinking, it is a harmonic blend of all that makes a good data scientist.
