How To Overcome the fear of not getting job in Data Science

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With the world revolving around the possibilities of new recruitments and career changes, it is only normal to be overwhelmed at the thought of not being hired at a job you have been looking forward to. The use of sophisticated technologies like machine learning and artificial intelligence, has created considerable number of positions for data scientists in the field. With consumers evolving and demanding innovative products to meet their comfort seeking lifestyle choices, Data Science is the need of the hour. Thus, data scientist as a career option cannot go wrong.

What does a Data Scientist do? 

Data scientists essentially extract insights from data using their expertise domain knowledge (e.g. Finance, Healthcare and Technology) applying Algebra and Statistics theories, which is further needed to clean the data and understand pattern for certain business problems. After which, a definitive approach through machine learning algorithm is applied to make predictive analysis.

Recent trends in Data Science jobs:

Data Science and Data Analyst jobs are among the most challenging to fill. Employers are willing to pay premium salaries for professionals with expertise in these areas as well. The study on the recent technical job recruitments reckon that employers are willing to pay a premium of $8,736 above median bachelors and graduate-level salaries. Experienced Data Scientists and Data Engineers are negotiating sales over $100,000.

Launching oneself into the professional world is no cakewalk. The best way to build up confidence is by starting small. Constantly engaging oneself in reading articles on how far it takes to land the job you’ve been dreaming about or how much years of experience will it take to mould you into a worthy data scientist in the industry will only foster imposter syndrome in you, making way for more fear and self-doubt.

Here are 7 tips to help you tackle the fear of not getting a job in Data Science:

1. Know what to take in and what to roll eyes on

IT industry specifically, gets a lot of attention and praise in the media. Given how often new tech innovations get covered in the news and how the people behind it are portrayed as brilliant and ingenious, it’s no wonder that so many people feel that they can never make it as a top-tier data scientist or developer. Data Scientist as a career option comes with an ample mix of options to explore within. Being held back on stories of people not making it through and giving up half way shouldn’t make you doubt yourself on what you set foot to achieve. Choose to offer ears to inspiration that can help you better yourself and not let your esteem down.

2. Accept that it is not easy, stay prepared

The market cannot be at its high point always. Landing a job is not as easy as it seemed to be when the industry was flourishing. There are a lot of things that we have learned to accept in life. Accepting reality simply means that you recognize that it is what it is without complaining or ruminating about it. However unfair and unpleasant the times are, believe that the ball can come to your court anytime now. Be prepared to launch your shot.

3. Be open to opportunities, don’t judge too early:

Data scientists are preferred by both start-ups and tech companies. In fact, it’s the start-ups that are increasingly becoming aware of data science, looking forward to hiring more data scientists than before. Corporates and tech companies are catching up by reinvesting on analytics and data scientists. Loosen up on demanding a multinational company job right from the beginning. While embarking on your Data Scientist career, kick starting with start-ups will only let you grow and help you weigh your experience scale up.

4. Acknowledge your fears and reach out to people

You don’t have to put on your best face when you’re clearly terrified about your career kick-starting or a major change happening in your professional journey. Let it out, talk to people about your feelings, your doubts. Don’t underestimate the importance of other people when you’re faced with the stress of unemployment. Social contact is nature’s antidote to stress. Nothing works better at calming your nervous system than talking to a good listener.

5. Keep up with the latest trends, your industry never stops evolving.

It’s an extensive field and it’s only growing bigger. There are more data scientist intakes happening every year. This is paving paths for creation of new frameworks, and database tools. This means there’s more to learn and it’s only going to get more complex as the industry matures. Instead of considering this a threat, work on updating your skills on the latest available tools and prepare yourself to face work-life competition from all sides.

6. Age and Gender Detection

Not always will looking out for advertisements help you find the job you are looking for. There’s a huge majority of job recruitments that are not always advertised but are mostly taken up through networking. Though it might sound intimidating for a fresher, when it comes to job hunt, connecting with people from your same professional background can enhance your exposure especially since you are a newbie looking out for opportunities.

7. Worry the least about your career option losing its charm

The demand for data scientists is at an all-time high and is showing no signs of decreasing. What is likely to happen over the years is that the role of the data scientist will change quite a lot. Data scientists have traditionally been math geniuses with PhDs. That’s already changing. There is now a lot more demand for people with practical experience who can apply well-known methods to data. We have a lot more tools at our disposal now, making the actual job of implementing machine learning algorithms much easier. People are starting to realize that you don’t need to hire PhD’s at insanely high salaries to get the work done. Even if we reach a high level of automation in machine learning, it is unlikely that data science as a career will go away. Data science is not just about algorithms. It’s about understanding the data, and we need people to do that.