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5 Tips Data Scientists Can Follow For A Smooth Transition Into A New Job


5 Tips Data Scientists Can Follow For A Smooth Transition Into A New Job


With Data Science being one of the highest paying jobs, more and more people are opting for this buzzing career. According to one of our studies, the number of jobs in analytics and data science ecosystem is 97,000. Out of these, 97% of job openings in India are on a full-time basis while 3% are part-time or contractual.



However, no career is without its challenges and the journey towards having a successful data science career is no exception — one has to have an immense amount of knowledge from the domain. Also, it's not just the knowledge, there are challenges too while joining a workforce as a data scientist on a daily basis.

In this article, we take a look at some of the most common yet critical challenges a data scientist faces while entering a workforce and how one can overcome them.

Adjusting With The New Workforce

This is the most common challenge people face while entering the workforce. And many people take a significant amount of time to adjust to a new place. Data Science is a vast and lucrative field of work and to be more productive a data scientist needs to have a good work environment — whether it is with his coworkers or just the office amenities. So, you enter an all-new workforce, it gets difficult for a data scientist to find his/her comfort zone and settle.   

In order to settle down well with the new co-workers, have a good introduction session with the fellow data scientist Also, discuss the new work dynamics with your colleagues and understand their pace of work. By doing so, you get to understand your coworkers as well as the work environment.

Getting Along With The New Work Pace

Compared to your previous gig, your new workplace might be fast-paced and may pose a new challenge to get along. This might be an easy task; however, you can still do it. Have a word with other fellow data scientists about how they manage their time. Up next, set your daily goals—whether it is data processing, cleansing, or verifying—make sure that you have all scheduled for the day.  By creating a "to do" or goals list, you can make your daily work more manageable, and also be more productive and efficient at the same time. Also, don’t hesitate to seek help when need as it will help you get your work done on time.

Dealing With Coworkers Who Disagree With You

Data science is all about extracting meaningful insights. There may be a situation where your new project manager or your fellow data scientist might not agree with the results you present. This can make you anxious and less productive. This is a huge challenge for data scientists who join a new workforce.

The ultimate solution to this challenge is to have a calm and meaningful conversation. Have a word with your fellow data scientist and discuss the problem that you were working on, instead of getting into an argument, try to get them to work together as a team and present a solution. When a solution is present by a team, there are fewer chances that others will disagree.

Getting Your Hands With New Tools

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Things differ from place to place. It is not necessary that every data science department in a different workplace will use the same tools and systems. So, when you enter a new workforce, you might see a lot of new tools, systems and process, completely different from your previous workplace. So, this might be a critical challenge for a data scientist joining a new workforce.

The only way to overcome this hurdle is to learn about the new tools — both by yourself and by seeking help from co-workers. You might have to do a little extra work for some time, however, it’s worth it.

Getting Along With The Your Supervisor

When you work in a fast-paced domain like data science, you might not agree with your supervisor on some issues. However, that doesn’t mean any of you are wrong. Situations like this are natural when there is a workload on both the parties. And when you are new to a workforce you get a feeling of proving yourself and you might want to take things as fast as possible, but shouldn’t be the case.

So, when you get into a situation like this, make sure, instead of proving yourself right, try to figure out a middle ground. You have to look at the problem from both your perspective and offer a solution that fixes it. Everyone has their share of the workload, so make sure you do your part best and deliver as much value you can.

Word to the wise: When you are a data scientist, you are expected to be exceptionally good with your problem solving and analysing skills. So, make the most of it.



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