What IT Companies Look at While Hiring a Data Scientist?

December 20, 2017
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.

Creativity

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.

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