As data science has been termed as the most preferred job of this era, breaking into the field is getting tougher day by day. Although, there are several graduate and postgraduate degrees, certificates, bootcamps, and platforms to opt for, in order to learn data science, the chances of getting a call or landing an interview can be challenging as well as frustrating.
Job hunting is tedious, and when it comes to the competitive industry of data science, the hustle gets even taxing. In this article, we list down a few best practices for a data science job search.
Forget Facebook, Join LinkedIn
If one needs to achieve or accomplish something in their life, then one needs to sometimes cut themselves off from the virtual world of Facebook. Instead, the real social media that could help an enthusiast to get a job, in the ever-challenging data science landscape, will be ‘LinkedIn’. One has to be extremely serious if interested in getting into this field, and therefore the first best practice for a data scientist is to get on LinkedIn and learn about the field, network with other data scientists, and one can even promote themselves and their projects on the platform.
Check Relevant Job Boards
According to LinkedIn, there are approximately 57000 data scientist jobs available in the market; however, interested candidates are still struggling to find the right job for themselves. The competition is massive, and therefore one should find relevant job sites to find a job in data science. Some of the popular sites that can help candidates include, LinkedIn, Glassdoor, Kaggle Jobs, StackOverflow, HackerRank, and Analytics India Jobs, among others.
Better Keep Your Resume Updated
Like for any other field of interest, to keep your resume updated is also a best practice for a data scientist, who is searching for a preferred job. Having an outdated resume not only pushes the candidate way behind in line but also will portray the lack of interest of the candidate. One should check and update their resume every six months to a year, if not more often if you are actively searching for a job. Also, for people who are transitioning into data science should keep their resumes updated with projects completed or new skills acquired by them.
Do Mock Interviews
Mock interviews work like mock tests. One has to practice interview questions or do a mock interview with their family or friends in order to learn how to answer difficult questions. Mock interviews will also help candidates to develop interview strategies, improve communication skills, and will help candidates learn the way of dealing with stressful situations of interviews, as well as boost their confidence. One should practice questions available online on different platforms and should ask the mock interviewee to give proper feedback. Practising a few hours of the mock interview every day, with the help of a subordinate, can help candidates in the real situation of interviews.
Also Read: Interview Questions For Data Scientists
Keep Working On Projects
Projects have always been considered an important part of a data scientist. If interested in the field, one should carry out projects despite the fact, whether once gets a job or not. Projects can be a great self-starter for interested candidates, and therefore one should list down project ideas and start working on them. In fact, according to experts, project-based learning is considered to be the quickest way to acquire data science knowledge. Some of the projects to showcase for 2020 are, interactive data visualisation, sentiment analysis, customer segmentation and recommendation, and fraud detection, among others.
Keep Yourself Updated On Data Science Trends
The field of data science is ever-evolving, and therefore to land on a job, one should always stay informed on the data science trends that are driving the growth of the space. To stand out of the crowd, having a knowledge of what happened the previous year, and what’s new in the field is exceptionally crucial. As the capabilities grow, data science is getting embedded into every industry, and therefore to be updated about the business and the disruptive trends revolving around the technology becomes important in order to figure out the industry and understand the digital future.
Network & Share
Networking and sharing are the two most important things to know more about any new upcoming field. Networking will help candidates to understand more about companies, jobs and the field altogether. These candid conversations with peers and experts can not only help the candidates to know more people but will also assist in doing a word of mouth promotion for themselves. It has been believed that networking can provide more information than online platforms. One should also go to conferences, meet-ups or events, to increase their connections. Although the process is slow, networking can provide candidates with the best return for their investment.
Try & Try Until You Succeed
It can get tough to get job offers from companies that interest you, and many a time, candidates face rejection from several companies. Not getting selected, or getting rejected is part and parcel of data science job search; however, one should keep applying more and more until they land on a job. It gets harder for newer candidates to get their foot in the door; however, rejection shouldn’t stop anybody from pursuing their dreams, and therefore a data science enthusiast should keep trying until they succeed.
Don’t Be So Choosy
Although data science is a highly paid profession, one should not be choosy in terms of their role and salary while starting initially. Also, for the people who are transitioning should accept entry-level jobs in the field, as this will provide them with enough exposure and confidence to work on more significant projects in future. If it suffices your means, on a longer run, starting slow with ancillary roles with lower salary will surely push the candidates towards a successful position.
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Sejuti currently works as Senior Technology Journalist at Analytics India Magazine (AIM). Reach out at firstname.lastname@example.org