How to land that top analytics job

Big Data and how its use is reshaping everything from marketing, customer service, sales and even national security, fills today’s headlines. According to a McKinsey Global Institute study (2011) the explosion of analytical work is creating a shortage of available workers in the field, and the search is on for the next generation of top analytic talent. McKinsey states that by 2018, the U.S. alone could face a shortage of up to 190,000 workers with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

So with analytics exploding how do you jump to the front of the line and land that top analytics job?

We’ve determined three main qualities make up the perfect analytics candidate. First, they need the obvious strong quantitative background in math, statistics, physics, industrial engineering or computer science. But it’s also important to have both business insights and strong communications skills.

I work for Enova, an online lender that uses the power of technology and analytics to offer credit alternatives to more than two million individuals worldwide. At our company, analytics teams are embedded directly into the business, frequently interacting with marketing teams, strategy teams and executives. They can do the analytics and then turn that model into an actionable business solution that they can communicate to stakeholders. Good people skills and the ability to collaborate in a business environment make a candidate stand out.

With that as a foundation, here’s some advice on how to make a great impression in the interview and land that top analytics job you want:

1. Reach for the future. It’s important to find a company with strong prospects for learning and growth. I would take a lower-paying job with a fast-growing company over a higher-paying job with a low or negative growth company. When considering positions, think five to 10 years out. It’s important to think about which job will make you better off 10 years from now as opposed to better off next month. 

2. Plus-up your resume with certifications and competitions. Certifications from INFORMS, SAS, CFA or other actuarial exams nicely complement your educational background. Also, Kaggle and other statistics or data mining competitions are a great way to show employers what you can do.

3. Open-source software. For the kinds of analytics work we do at Enova, experience with open-source is a great asset. Contributing to one of the open-source data analysis tools that are available really makes a positive impression.

4. Show work samples. Academic papers and samples of a project you worked on are also valuable. In addition to your resume, show off your portfolio and be prepared with short yet exciting explanations of your projects. 

5. Develop a logical approach to interviews. It is important to demonstrate the ability to frame problems in a logical fashion. In discussions of actual work projects during the interview process, even if you don’t have experience in a particular field or type of project, outline the approach that will lead to a business solution.

6. Show your analytical mind at work. Ask questions during interviews, meaningful questions that show your inquisitive approach. Ask about the company culture. Ask the interviewer about his or her personal career path and how someone advances in the company. In some companies, employees cannot advance until a certain amount of time has passed or they reach a certain milestone. Ask about turnover percentage. Is it a place where people have been for 10 years or where they stay a year or two and then leave? It’s important to uncover this information before you take a job somewhere. Not asking questions is actually a red flag with someone interviewing for an analytics job.

One piece of caution: Don’t ask overly technical questions, like whether an interviewer prefers one really specific modeling technique over another. This usually doesn’t impress them and is more a missed opportunity to ask a more meaningful question.