10 Tips for Entry-Level Analytics Professionals

It’s no secret that analytics and data science are the hottest fields to get into right now. But getting into the field can be tough, and even with a stellar degree, it can be tricky to navigate the quantitative job landscape as an entry-level professional. Below are items recruiters look for in early career professionals, as well as tips that may help you in your initial search.

1. A completed internship. A great way to test your skills, continue learning, and expand your network is to complete an internship (see www.amstat.org/education/internships.cfm). Without previous work experience, prospective employers will look at internships (and coursework) to determine if you might be a good fit for their organization.

2. Experience with large, real-world data sets. One of the biggest challenges students will face in their first analytics job is lack of experience with messy, large, real-world data sets. It is crucial that you find a way to add this to your experience through MOOCs (massive online open curriculum) such as Coursera or Udacity, internships, coursework, or Kaggle competitions.

3. Kaggle competitions. Kaggle hosts data-crunching competitions in which you can practice your skills; compete against other members; and gain access to large, real-world data sets similar to the ones you might use at your first job. It is a great resource, and employers often view Kaggle experience similar to how they would view coursework or internships.

4. SAS certification. Although the availability of tools to wrangle Big Data has been diversifying, many employers still use SAS—and many look to the certification to verify credibility of analytical skills.

5. A LinkedIn profile. More than 90% of recruiters who use social media use LinkedIn. It has become the go-to resource for many companies wishing to check your references and résumé. Having an updated, professional profile allows companies to see you as a person they might want to hire, not just an anonymous résumé.

6. An advanced degree. In “Burtch Works Study: Salaries of Predictive Analytics Professionals,” 86% of analytics professionals had at least a master’s degree and 18% had a PhD in data science. Among data scientists, 88% held at least a master’s degree and 46% held a PhD.

7. Familiarize yourself with the industry. Learn about the key players in your industry and what the latest tools and techniques are. Also, stay aware of industry news that may affect your opportunities or the companies you’re applying to.

8. Research companies. As well as knowing more about the industry you’re targeting, you should make sure to research the companies you’re applying to. Knowing about changes in business strategies, corporate goals, and current events are all ways to show you have business savvy in addition to technical chops. Companies like to hear you are well informed, because this shows you are committed, interested, and willing to learn as much as you can about their needs and concerns.

9. Read job descriptions. Looking through job descriptions is a great way to get a feel for what technical skills companies want and indicate what else you may need to learn.

10. Network, network, network! Although it may be daunting, networking is a great way to learn about opportunities. Check out your local chapter of the ASA, join other industry groups, or attend local meet-ups to network with professionals in your field. Once you have completed your LinkedIn profile, you can add everyone you meet as a connection and join relevant LinkedIn groups.

For more information about job interviews, references, and strengthening your communication skills, check outBurtch Works’ blog. Best of luck with your endeavors, and make sure to connect with me on LinkedIn!

This article was originally published on STATtr@k by Katie Ferguson.


Katie Ferguson spent six years as a recruiter and human resource generalist for the City of Chicago. As a specialist in quantitative marketing science and operations research, she helped launched Burtch Works’ mid- and junior-level analytics practice and has been working with analytics professionals for seven years.


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