/ Guest Post

Preparing for the Data Science Job Hunt

Editor's note: This piece was written in collaboration with SwitchUp, an online platform for researching and reviewing technology learning programs. Erica Freedman is a Content Specialist at SwitchUp.

Job hunting is stressful, especially if you’re moving into an entirely new field. In this post, I give tips on finding data science jobs, looking up salaries, and what to do before you apply.

How to find data science jobs

You’ve likely heard of traditional job board sites such as Indeed and Monster, but there are also boards dedicated to data science. A slew of data science and technology focused job sites have come on the scene in the past few years, making it easier for you to locate and land positions that align with your skillset and passions. Check out these sites:

  • KDNuggets focuses on data mining, big data, business analytics, and data science. It has a detailed job board that is updated every two months. Because the site is data-focused, the majority of these jobs listings are applicable to up-and-coming data scientists.
  • icrunchData is a technology, data science, and analytics platform that regularly posts jobs in these three spheres. You also have the option to receive job alerts that arrive straight to your inbox.
  • Kaggle is one of the most well-known for data science job listings. Considered the largest community of data science and data-driven professionals, Kaggle holds the listings for most of the major players in the tech-space, including Facebook and Amazon.
  • Data Science Central posts jobs in data analytics, data science, business intelligence, data engineering, and statistics. Simply upload your resume to create a free profile.
  • DataJobs has a filter that allows you to find positions in data science as well as data technology.
  • DataWerq is focused on big data jobs for companies such as Google and Slack.
  • Data Science Report is useful later in your career. It primarily targets individuals at a senior data science level.
  • Digital Analytics Association is good for people looking for a job in digital analytics.
  • R-users is specifically for R-related jobs.
  • Data Elixir is considered one of the largest data science job boards currently online. Use this site to find everything from internships to full-time positions.

How to look up salaries

By understanding the average salary for your potential position, you can ensure you are being paid a fair wage while simultaneously showing prospective employers you’ve done your research. This sites can help you prep:

  • KDNuggets, also listed above, regularly publishes research articles that analyze the data science workforce. Look at this piece for more data science salary information.
  • Indeed has a filter to help individuals find data science job listings, and also includes a graph showing average salaries. Depending on your position, Indeed can help you see what another individual is already making. The only downside is that the filters are limited and can make it difficult to establish location searches as well as years of experience searches. Also, because this salary tool is self-reporting, it is not necessarily an accurate look at the industry as a whole.
  • Glassdoor makes it easy to see the average salaries reported by data science professionals. It offers a similar graph system to Indeed and is divided by position title.
  • Payscale is defined on their website as a platform that helps employers and their employees understand the right pay for every position. Through this platform, employers and employees can effectively communicate about compensation. PayScale pioneered the use of big data and unique matching algorithms to power the world’s most advanced compensation platform. This platform helps empower individuals to ask for the pay they deserve.
  • Tech Republic and WIRED also offer articles and research that can help solidify what data scientists are being paid throughout the United States.

Before you apply

Finding your job prospects and worth are important, but they are useless if you aren’t able to land an interview. Follow these steps to ensure you’re prepared for any application.

Practice

Most data science jobs require live problem-solving and coding on a whiteboard. Be sure to practice sample problems as well as projects. Similarly, review past projects that you have built and stored in online platforms such as GitHub. Know what you have done and practice what you may have to do.

Create an Online Presence

In 2018, an online presence is no longer optional. Be sure you have a detailed and polished profile on a variety of sites. I've listed some ideas below.

  • LinkedIn allows you to connect with influential individuals in your field. Think of it as an online networking tool that every potential employer will look to see you have.
  • GitHub is an important one. According to IFS Labs, “There are now about 1.5 million developers hosting their projects on GitHub,” and you should be one of them.
  • Quora is a question and answer website, but it is also a great tool to establish authority in your field. Answer questions to help your community and also gain visibility for your website or blog.
  • Using Medium or creating your own blog will help you stay relevant, grow your portfolio, and show potential employers your varied talents.

Do Your Research

Study the company you are applying for:

  • Make sure you explore their social channels.
  • Understand how they speak about their company or brand.
  • Explore the profiles of current or past employees.
  • Read the about section on their website.

Interviews are a two-way street. Be sure you are as interested in the company as they may be in you. Make sure your passions are aligned before dedicating the energy to applying.

Wrapping up

As you enter the job market, these tips will give you the boost you need to stand out from the competition. By focusing on new ways to build your portfolio and online presence, you’ll discover the best practices for marketing yourself to employers and landing the data science job you want.