Hiring data scientists (part 2): the perfect candidate doesn't exist

[This is part 2 of my series on hiring data scientists. If you haven’t read part 1 yet, do that first.]

In part 1, I talked about all of the skills I want in an ideal candidate. But since that candidate doesn’t exist, I have to prioritize what attributes the candidates that I hire have.

When you look at it like this, it seems reasonable.

The ability to get things done [required]

Getting things done means that when I give you something to work on you will:

• Start working on it, and
• If stuck, ask for guidance.

This is more important than any amount of technical of business skills because it builds trust. I don’t care what someone is capable of if I can’t trust that they’ll actually do it. If they don’t know how to ask for help or are too afraid to, they will burn time waiting until I notice things aren’t getting done.

Intelligence [required]

I need someone who can realize when they have a knowledge gap, and then has the motivation and skill to fill it. If a candidate can’t notice their own shortcomings and overcome them, there will always be a limit to what they can do. I want a person who has the ability to take a task, realize it can be improved, and then teaches themselves how to do it. For instance if each week they’re required to run a weekly report, I want them to notice that someone could automate it. I then want them to have the ability to learn how to automate it themselves.

Programming and databases [important]

Programming is the most important hard skill I hire for. It’s essential for advanced modeling, working on large datasets, and interacting with APIs. Programming requires deft thinking and constant application of new tools. The ability to code doesn’t just show me the person can write a program, but it shows me they have some skill in learning, solving puzzles, and thinking critically.

Math and statistics [optional]

It feels dirty to say this, but it isn’t necessary to know math or statistics to be a good data scientist. Someone can aggregate data and make slick visualizations without knowing what a linear regression is. Most candidates who can get stuff done, are intelligent, and have programming skills can pick up math and stats as they start working, moving from exploratory data analysis to model building. Sure, they may never be able to fine tune a deep learning model, but data science encompasses so much more than that.

Business expertise [very optional]

Business expertise is the skill I am most happy to shrug off. The ability to work and communicate with others will be picked up after having spent a few years working within a company. And if they never do? That’s fine. There’s plenty of value in being a team member who builds models and cleans data without reporting out.

Data science archetypes

When you spend a good hunk of your time interviewing for data science positions, you notice archetypes emerge. Here are my highly-stereotyped classifications I’ve observed again and again, as well as some advice for what to do if you happen to fall into one of these categories.

The business analyst

This person does most of their work in Excel. They take data and manipulate it into charts, then make PowerPoints from these and present them. Maybe they pull their own data using SQL, and maybe they don’t.

• Pros: They have business expertise. They know how to make a good looking chart that lands with leadership.
• Cons: They don’t have experience in programming. They’re comfortable with using Excel and SQL only, and may not have the ability or desire to learn programming. The fact that they are into their career and haven’t learned programming is a red flag. 🚩
• Would I hire them: Yes, if they have the intelligence and motivation to learn to program.
• If you are this person: Learn to program! There are tons of courses out there on how to learn R or Python. Take some of these and then do something with that knowledge. Anyone can watch a presentation, but using your new abilities to do something like compete in a Kaggle competition is what will set you apart.

The business intelligence person

This person sets up databases and creates dashboards to present business metrics and KPIs. Other people take their dashboards and use them to make decisions.

• Pros: They know how to work in SQL, and how to make pretty charts. They get things done!
• Cons: They don’t know how to take the data they present and infer meaning from it (that’s someone else’s job). They don’t know how to program.
• Would I hire them: No, unless I see evidence that they can learn how to both program and draw insight from data. BI and analytics have totally different roles. BI is to set up the data backend, analytics is to use that data to make decisions. A BI person needs to prove that doing analytics won’t overwhelm them.
• If you are this person: If you want to be a data scientist, move to the role of business analyst first. This will give you a small taste of what data scientists do. If you like being a business analyst but wish you could go deeper, then consider data science.

The data scientist

This person knows how to program and how to build machine learning models. If their only experience is building production models, chances are they don’t have business expertise and instead build the models that other people tell them to.

• Pros: They’ve done this job before! In theory they should do great on my team.
• Cons: They may be out of my price range. They may be bored by doing mundane data cleaning or exploratory analysis. They also may not have experience in presenting and working with a client.
• Would I hire them: Yes, but I have to balance my team so it isn’t filled only with people who make models and do deep data science. I need some people to do the more mundane stuff too.
• If you are this person: congratulations you will have no trouble finding a job somewhere. You probably know that already.

Next, the archetypes for people coming from school and looking for their first job.

Person coming out of school with a freshly-minted STEM degree

Hopefully this person has a degree in math, statistics, or CS, but as a substitute economics, physics, or engineering will suffice. They may have had an internship or two, but they haven’t worked full time before.

• Pros: They’re eager to learn new skills and do well. They often have some mathematics and statistics, and have done a bit of programming. They are willing to do more “grunt labor” tasks.
• Cons: They don’t have any business experience. They haven’t had their spirits broken by the harsh realities of the 9ΓÇô5 job.
• Would I hire them: Absolutely! These people don’t take as long to ramp up as you would think, and want to succeed. Before hiring them they need to show that they can get things done, like having had an internship, a side project, or a particular compelling class project.
• If you are this person: Having a high GPA isn’t important, showing that you can get things done is.

The academic

This person was on an academic track but is exiting it for industry. This could be from leaving partway through a PhD program to having had a post-doc.

• Pros: Knows math and statistics and how to program. Is very intelligent.
• Cons: Academia doesn’t teach you how to get things done. If they wanted to go to academia then they enjoy working on the most intellectually stimulating problems for the thrill of it. That’s very different than working on the problems that are most important to a business.
• Would I hire them: No. If I were to hire them they would probably be unhappy working under me. This job has lots of time spent doing uninteresting tasks like data aggregation. During the first few months, they’re probably going to spend a lot of lunch breaks alone in their car screaming about how meaningless it all is, and I don’t want to be there for that.
• If you are this person: Before you leave academia go get an internship or industrial experience. That will show you know what working a 9-5 job is actually like and you are still into it.

MBA with a business analytics focus

This person got an MBA, but wanted to learn about what’s relevant these days so they got a degree that focuses in analytics. Business school taught them how to use Excel and use plug-ins to do things in it like *k-*means clustering.

• Pros: potentially has more business acumen than someone with a STEM degree.
• Cons: Their coursework may have taught them a few data science techniques, but not the deep understanding of how they work and why. they wont know how to program.
• Would I hire them: No. Having a business analytics focused MBA is a signal that they didn’t want a math, statistics, or data science degree. It’s a lot easier to teach someone who knows how a logistic regression works to use it on business data than to teach someone who understands business how a logistic regression works.
• If you are this person: You are qualified for a business analyst job, and there are lots of those! You’d be happier with one of those than with a data science job. You can start as a business analyst and work your way up if you so choose.

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So to recap: there are a ton of different people applying for data science positions, and some types of people have more of the skills I want than others. But how do I figure out what knowledge and expertise the people actually have? That will be covered in the upcoming parts of this series:

• Part 3: What questions do I ask during the interview and why?
• Part 4: What case study do I give and how does it help?

If you want a ton of ways to help grow a career in data science, check out the book Emily Robinson and I wrote: Build a Career in Data Science. We walk you through getting the skills you need the be a data scientist, finding your first job, then rising to senior levels.