5 Warning Signs that Turn Off Data Science Hiring Managers

Here are some warning signs that will prevent managers from hiring you for a Data Science position. If your resume has one or more of them, make an effort to remove the risk factors.

By Kaiser Fung (Junkcharts).

hiring-decisionAs a hiring manager for data analytics positions, I often complain that there are not enough qualified resumes. Most of the resumes that do get passed on to me from recruiters quickly get filed away. Those job candidates belong to one of five high-risk types that I have identified over the years.

These high-risk candidates do not make the cut despite having technical degrees or technical work experience. If your resume falls into one of these types, you should make an effort to remove the risk factors.

1. You have no real-world data experience

For hiring managers, exposure to real-world datasets is a strict requirement. By real-world data, I mean data with missing values, data with duplicates, data with weird distributions, data with typos, data with erroneous time-stamps, and data with no dictionaries. Real-world data require days, weeks and even months of wrestling to tame, after which you inform your boss you are ready to start the analysis.

Did you remember spending weeks at a time cleaning datasets at school? I bet not. If the class is about teaching logistic regression, the professor has no interest in torturing you with dirty data. Many of the public datasets have become so popular that the major problems with the data are well-known (and sometimes corrected), so the best way to train yourself is to find new datasets.

Because so much of an analyst’s time is spent wrestling with data, the hiring manager won’t take a risk with someone who do not have scars from tackling real-world data.

2. You have no business experience

Data analytics is a business discipline. The goal of data analytics is to use data to make better business decisions. Analysts must work in sync with corporate goals. Their first job is to figure out which business problems can be solved better using available data.

People with technical training and technical work experience frequently have no exposure to the business issues. You do not need a full MBA education but to convince hiring managers, you have to demonstrate command of basic business topics, such as cost-benefit analysis, customer life cycle, and so on.

3. You have no accomplishments

How is it possible that your resume contains no accomplishments? You might wonder whether I need new reading glasses to locate the dozens of bullet points.

The truth is most resumes I come across are long lists of job responsibilities with no actual accomplishments, by which I mean the creation of business value. “Use Python to run statistical models to predict customer churn” is a job responsibility, a task that your boss assigned to you. And this same task is assigned to many other people by myriad bosses at myriad companies.

If your resume reads like a job posting, you have a problem.

4. You are in love with one programming language or software

Of course, if we are both in love, then you are golden. But most companies have a history, having invested in certain systems, processes, and software. They may not be the one you are in love with. (Plainly speaking, if you only know R or Python, you are limiting your career options.)

I learned this lesson the hard way. I thought that if someone is willing to invest the time needed to master one programming language, then this person would find it easy to pick up a different language in the same discipline. The reality is the more expertise one has in one language, the less incentive one has to switch to something else.

So if a resume is too focused on one language, and that language isn’t the primary one for my team, I take a pass.

5. You have many short-term data jobs

Data analytics is a craft. This is where theory meets practice. A craft isn’t something one can master in a few months. In fact, it takes up to a few months just to learn about the data, and then another few months to really learn about the data by manipulating and cleaning them. Then, for a project to get executed, resources need to be secured from other teams, after which time is required for development and testing.

So I am very suspicious when a job candidate spends less than one year at an analytics position, and then have multiple bullet points to describe that experience.

If you look at your resume, and find one or more of the above five characteristics, you are striking fear in hiring managers. We are afraid that we have to invest in training and development, and not sure about the pay-off.

To overcome this problem, you should broaden your resume, such as learning about business basics and learning different software. You should also consult career counselors who can help you sharpen your pitch to hiring managers.

Bio: Kaiser Fung is a Marketing and Advertising Analytics expert, author and speaker. Currently at Vimeo and NYU.

Originally published in rSQUAREedge, which connects data science & analytics leaders with people seeking to enter the field.