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6 Factors Data Scientists Should Consider Before Switching Jobs

6 Factors Data Scientists Should Consider Before Switching Jobs

Data Science is creating huge job opportunities in the coming years. Several organisations are providing good compensations to data scientists, yet they are facing challenges when it comes to hiring data scientists.

How To Start Your Career In Data Science?

Our recent report on hiring scenario in Indian organisations gave detailed insights on hiring trends, skills required, educational qualifications that companies are looking for. It also revealed that there is an acute talent shortage of almost 1 lakh open analytics positions.

The tag of the “Hottest job profile” can lure you to become one but there are sometimes when you think to make a job change in your career. Before you dive in and make a job switch, there are 6 crucial factors to consider before moving out of your present organisation.

1| Choosing The Right Track

To be a successful data scientist, one has to know the ins and outs of this profile. There are basically three tracks in data science work. Data scientist with analytics, a data scientist with algorithms and a data scientist with inference. Data scientist- Analytics are for those who are skilled at asking a great question, exploring cuts of the data in a revealing way, automating analysis through dashboards and visualizations, and driving changes in the business as a result of recommendations. Data scientist- Algorithms are for those who are expert in machine learning, passionate about creating business value by infusing data in our product and processes and lastly, Data scientist- Inference is those who are statisticians, economists, and social scientists using statistics to improve our decision making and measure the impact of our work. If you are working as a data scientist but the company has a different track than your comfort zone, you should think for a job change.

2| Limiting Toolkit

To become a successful data scientist, you may have learned various skill sets. Right from multiple programming languages to various machine learning techniques, you have acknowledged all the skills needed. However, the organisation you are working on have made you work on a specific skill and you are handling the same problem every day and started to feel stagnant in your work, this may lead a data science enthusiast for a job change.

3| Less Growth Opportunities

The emerging technologies are growing in a very fast manner and to be in the rat race, one has to keep the same pace as the advancing technology. If an organisation is working with traditional tools and technologies, then it becomes hard to shift with newer technologies, yet it is important to mention that many of the organisations now are trying hard to make the big shift. In this competitive arena, a data scientist in an organisation which provides less updated skills will face a harder time in his career.

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4| Lack of Professional Development

As a data scientist, you might have gained insights on various programming languages, machine learning techniques, learned various visualisation tools with an aim to be fruitful in the organisation as well as grow in your career. But when the organisation in which you are working on isn’t giving you the flexibility to use the tools you want, that is the time a data scientist can think for a job change.

5| No Salary Hike

Being the hottest job profile, it is already known that the pay is much higher than any other usual jobs. There is a general term known as “job switch” by which professionals switch to other companies to get a salary hike. If you are a data scientist and looking for an increase in salary, it would be wiser to change the company rather than working there for many years.

6| Different Company Culture

It is common to have an incompatibility between the employee and workplace peers. According to Bruce A. Hurwitz of Hurwitz Strategic Staffing, Ltd., “A company’s culture is the ethical/behavioral framework within which a company operates.”Hurwitz points out that cultures can be quite different from company to company—from a very conservative IBM model, where the dress code is the blue suit, white shirt, red tie, to a much more liberal culture in a place like Google that has an extremely casual dress code. Rather than a dress code, there are also other things to notice such as attitude actions, behaviors, etc. to take into account for the company’s culture. If your preferences are not compatible with the company’s culture, it is a smart decision to change it.

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