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What Do IT Companies Look at While Hiring a Data Scientist

Data Scientist

The position of a data scientist is most sought after in recent times, mainly because of the boom in information, and the quick need to make sense of the data, extracting insights which could positively impact business. The buzz around data science, at times, confuses the link between the candidate and the company, creating a gap between what the company needs as opposed to what is perceived by the applicant. So essentially what an IT company might be looking for in a data scientist might not be very different from what another line of business might be looking for in a data scientist.

It could be true that a tech company might be absolutely aware of the specific technical skills, required and their job description might be more evolved as they have the other skill sets in place, or have the functional knowledge about expectations out of qualifications, which for a non-tech organisation, might have blurred boundaries.

Educational Background

Irrespective of the industry, one solid fact that will be looked for while hiring a data scientist is the educational background or the technical know-how. Traditionally a data scientist needs to have a strong foundation in statistics or mathematics, this will be an added advantage, specifically from an IT point of view, as they would have many engineers with the Machine Learning capabilities, but a strong foundation would ensure that the data scientist will use or create the right algorithm by understanding the technical details.

Programming Skills

It is a given that a good data scientist will/should be an excellent programmer. Activities such as Sampling, Pre-Processing data, Model Estimation & Deployment, Sensitivity Analysis, Back-Testing, etc…, are used frequently by a data scientist so that these steps are successfully performed, programming needs to be done. Hence a data scientist should have sound working knowledge of prototyping languages like SAS, Python, R, Deployment Language such as C++, C#, etc…, and lastly big data languages like Scala or Spark.

Exceptional Communication and Visualisation Skills

At present, there is a huge gap between analytical models and business users or stakeholders. It is imperative that the data scientist not only understands the business know-how, which he or she is associated with, but also explains the analytical models along with the involved statistics, and reports in a non-technical manner to the stakeholders. If this is successfully done then the business user will be able to appreciate the advantages of big data, which will further improve their acceptance and attitude towards big data.

Line management skills are also what sets apart a data analyst from a data scientist, the ability to work with people and not in a silo is imperative.


Finally, anyone looking to hire a data scientist, in some sense expects them to be creative. They need to be creative on the technical level, with regards to feature selection, data treatment etc…, the steps of knowledge discovery process, or the ability to take the right guess or select the right approach makes a huge amount of difference, after all it’s the same data set, how you treat it is important. Not only technical creativity but also the know-how about the ever-evolving

Data Scientist

Data Scientist

data field, where they are up to date with current and future possibilities and technologies is important.

So a data scientist should literally be a master of all, with a mix of skills ranging from programming, quantitative modelling, communication, visualisation, to business acumen and creativity, as the field of data science and analytics is multidisciplinary in nature.

To make a career as a Data Scientist opt for an online data science course, which you can do by seating at home.

This program is co-created with Genpact as Knowledge Partner. This program helps you with a deep understanding of Data Analysis and Statistics, along with business perspectives and cutting-edge practices using SAS, R, Python, Hive, Spark, and Tableau.

  • December, 20th, 2017
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Data Science – Things You Should Know

Data or Big Data is the new buzz word. No matter where you are from, across all lines of work, you cannot deny coming across Data Science. You might not understand data science and all that comes along with it, but you cannot deny that the impact of Data Science is enormous, and it mostly changes things around it for good.

So where is this data coming from and why now? Now a day’s over six billion devices are connected to the internet, all the applications and devices that are connected have users and their movement on the internet generates data, it is estimated more than 2.5 million terabytes of data is created in almost 24 hours. That is humongous!! And as technology keeps getting added into our daily lives, these volumes are going to increase exponentially.

This is the fascinating piece of information, especially if you are from the field of IT. More so because recently there have been massive layoff drives in India within the tech companies. Like the law of nature goes, it is time for IT professionals to evolve or perish, and what better time than now to dive into the world of data science.

So if Data Science is of interest to you, or for a quick understanding of the subject, read along.

Data Science is coined as the ‘Sexist job of the 21st century’ by the Harvard Business Review. Since there are such huge volumes of data that is created on a daily basis, you need the skills from the data science field to get insights from this information and set things on track.

Data science is used to Optimize Performance. So if you use the GPS or make online purchases, do you notice how the next time you try to move online, you see the internet throwing the right recommendations? The data that you are generating online come back to you as optimized performance, due to data science insights.

If you wish to progress in this field, there are certain skill sets you would need, like Statistics, Knowledge in Data Science Tools, a Business Acumen, excellent Communication Skills, Inquisitive and Analytical Mindset, skills to successfully work on data, ability to find patterns and extract information.

Good news is that you don’t necessarily need to have a degree like a PH. D, however certification in the fundamentals of analytics along with the required technical skills should be a good starting point.

Since Data Science is a vast landscape, possessing all working knowledge about it is not possible, knowledge in globally recognised technologies like SAS, R, Python, SQL Database and Hadoop will make it easier for you to make a switch or enter the field of data science.

Data science requires niche skills and deep understanding of analytics.

Analytics can be broadly classified into three categories,

(a) Descriptive Analytics – as the name suggests, is describing the pieces of information that are uncovered from the data pool, for E.g. while analysing how many people are interested in pursuing a career in data science, descriptive analysis would say 35% people from IT, 27% graduates from Statistics etc.., are interested.

(b) Predictive Analytics – again as the name suggests, forecasting about the events that could happen based on historical data. So essentially with predictive analysis, one could estimate how many people from IT will enter the field, by studying the past turnout.

(c) Prescriptive Analytics – gives you solutions or prescriptions in rectifying actions for desired results.

Machine Learning is another upcoming vertical in data science, to simply put it, it’s the ability of machines to learn with minimal programming efforts by the method of algorithms.

Internet Of Things (IoT) is another technology which is contributing significantly to the field of data science. It is basically an ecosystem of devices which are connected with each other via the internet. IOT is all about data generation and data science is all about data analysis.

Learning data science is not sufficient but you also have to practice it. Look for courses that offer case studies and projects with real-time data sets to work on. You will need this upper edge to have a rewarding career in data science.

  • October, 4th, 2017
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