Despite the alarming state of the larger job market during the past several months since March 2020, there are luckily still thousands of openings for data scientists, machine learning engineers and data analysts in a variety of different industries worldwide.
Even before the COVID-19 pandemic, many companies already had widely distributed and remote work situations, making the transition to a fully remote workforce far easier to implement and maintain.
Telecommunications and internet technology has also advanced considerably in recent years, making successful remote work possible from virtually anywhere.
The rise of big data
Combine the ability to work remotely with the huge troves of diverse datasets from the increase in global web traffic over the past few months, and it is no surprise that many companies have actually decided to expand their capabilities to process, understand and interpret these vast stores of data.
This is where data scientists and the skills that they bring in their toolbox really come in handy! Data scientists bring a diverse skill set in computer science, applied statistics, software programming and data visualization to understand trends and make predictions with the many data sources available.
Even though there are many new job openings, data science has also received considerably more interest in colleges and universities resulting in increased competition for positions, making it particularly important to apply the right strategies to help you succeed.
Strategy 1: Make sure to cast a wide net across job search and career websites
This first strategy is especially important in this day and age during the 21st century, when there are numerous job posting websites out there and not all employers pay to have their positions on all job posting sites.
Using a website like GlassDoor or Google’s job meta-search will ensure that you are able to access all of the career websites at the same time and are not only limited to one set of job postings from particular companies.
Casting a wide net also applies to data science jobs to apply to as well, meaning that you should still consider applying to jobs that require slightly more experience than you already have or that desire similar skills to your skill set.
Remember, you can always spend a full day studying up on a statistical method or function within a software program before any actual interview, so don’t let very specific job qualifications hold you back.
While applying to many more positions that are completely unrelated to your background and skill set is counterproductive, job postings often encompass more desired qualifications than are actually necessary, so also don’t get discouraged by complex requirements in job postings.
Strategy 2: Stay up-to-date on the desired qualifications in the industry & seek relevant data science training for in-demand skills
A second strategy for success in any data science job search is to make sure that the skills that you are learning align with the desired qualifications in recent job postings for employers that you would like to work for.
The key word here is recent, because the data science, statistics and programming skills that employers actually seek change every few months to years, depending on the particular industry.
In fact, a great way to get ahead of the competition is to check recent postings for data science positions on GlassDoor or Indeed, write down the specific software skills desired and then take the steps to learn and master those programs.
If you find a company that you are very interested in or that you want to apply to but that doesn’t have any openings, try finding and messaging current employees on LinkedIn to get a feel for programs and skills that are in demand.
If you do secure a phone or video interview, make sure you browse sites like GlassDoor and even Reddit, to get insight into whether the interview is more concept-based, or if the company requires you to complete a data-science task and/or solve a problem during the interview itself.
Strategy 3: Build a professional portfolio to submit with job applications, including your code script files, visualization outputs & data-driven reports
Whether you apply for a private-sector industry data science job or a non-profit research position, you will likely need to provide samples of the real-world application of your data science skills.
To help you be prepared way ahead of time and to keep track of your best work, it is an excellent idea to create a separate folder on your main desktop or laptop that contains examples of your best data science work.
These files should include R, Python, Stata or SPSS code samples from course exercises or actual professional projects, examples of presentation slides with visualizations you have created, and any data-driven reports that showcase your data science skills.
Even better, take your portfolio public by signing up for a GitHub account, which lets you post code files for any programming language, share code and datasets with other data scientists, and learn more about the wonders of open source software programming.
For specific tips on how to create your own professional data science project portfolio, this tutorial is a great place to start and also check out this helpful article from Medium that covers how to create a competitive portfolio that can help you land the job you want.
The bottom line: Help your transition into the exciting and growing data science field with these 3 strategies
While these strategies will help, they will only help you to succeed if you also remain persistent through difficult to learn topics and stay open to learning new ideas.
Data science isn’t easy, yet we hope that these strategies will help you along your data science learning journey and beyond in a career in data science.
If you found this article helpful, don’t miss our recent article that covered 5 tips to help you succeed in a career in data science and to make sure you have the latest resources for data science, check out our data science tech guide and reviews of the top 5 books on data science and statistics.