Jessica asks, “what should be the interview questions when hiring a good data scientist?”
The answer to this question depends heavily on how fluent you are in the language of data science, in order to sniff out unqualified candidates. Focus a lot on scenarios, and work with a non-competitive data scientist to build out questions and answers, and listen for a specific magic phrase that indicates a data scientist’s actual skill. Watch the video for details.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Jessica asks, what should be the interview questions when hiring a good data scientist? Okay.
The answer to this question is going to be very heavily dependent on what your fluency is in the language of data science because ultimately we’re trying to do is you’re trying to find a qualified data scientist who can address your company’s problems, likely scenarios, likely types of solutions they would pursue.
And there’s a lot of people out there who you know, they did the whole six week crash course in data science thing because they see the average published, you know, salaries of data scientists and say I want I want a part of that.
I don’t blame them.
But they are is a huge Huge amount of difference between somebody who has been living in data for years, if not decades of their life.
And somebody who took a six week Crash Course is the difference between somebody who is an actual surgeon, and somebody who took like, you know, a Red Cross first aid course they’re, they’re both people that have a place, right? You want people who have some first aid training? Absolutely.
You don’t want that person doing neurosurgery.
If your company has first aid problems, only then that first aid person might be just the thing.
So what kinds of questions are we going to be asking? Well, here’s the thing about data science.
Actually, this is true about any profession, the sign of expertise, the sign of of experience, and a wisdom is not knowing the answer to things because you can find the right answer to a lot of things.
is knowing what’s going to go wrong.
So what I would suggest you do is you work with a data scientist, maybe someone in a non competing industry, you’re not going to hire them on a fee, you do great.
You’re not going to hire them.
What you’re going to do is work with them, you know, buy them something, get them a gift card, pay them by the hour, whatever.
To help you work out interview questions that are specific to your company in your industry.
Let’s say you’re a coffee shop, right? What are some data science questions that you would ask about a coffee shop scenario? Why’s that you could ask to get a sense of what are the challenges you’re likely to run into? So for example, if you’re that coffee shop, and interview question for a data scientist might be we have all this customer data and we want to build a model to predict to predict the customer propensity to buy I don’t know school.
With their coffee, tell me how you would approach this problem.
What are the things you would do? And then, based on that solution, tell me what’s likely to go wrong.
Right and see what the person answers.
When you’re working with your qualified data scientists to develop these questions, they can give you the answers like, okay, you’re gonna ingest your customer data, is the data good? Is it clean? Is it ready to go? Or is it a hot mess in five different systems behind the scenes? What demographic data do you have? Is there potential for a human bias along the way, like, for example, if you’re, if your barista is racist, you’re gonna have a skew in the data because they refuse to sell scones to short people, or to Asians or whatever.
Those are questions that your data scientists is going to ask you, that will indicate the things that are likely To go wrong, okay, you’re building your model.
And in this model, how many highly correlated variables are there? How many near zero variables are there? There’s too many of them, you got to clean some of those out.
What is the predictive power of any of these other features? What other features do you have in your data set? Are there external conditions that we need to know about? For example, was the are you closed on Sundays? That would be an important thing to know.
And then in the in the construction of this model, how much how accurate is your sales data? Do you tracking every single purchase or are there things? Do you have a leakage problem or shrinkage problem like you know the, your inventories are off because your barista gives a free scone to each of the friends who comes in.
All of these things are things that go wrong in your data and can go wrong in your analysis.
And when they come up with the answer, they’re gonna, they’re gonna have to give you some clarification like, Okay, so in this case, you’re going to run probably multiple regression model unless you have so many weird karlitz that you need to look at like Ridge or lasso regression.
And even after that, if your predictor importance is below point five, you’re gonna have to find something else, or you have to acknowledge that there is a likely probability that you can’t predict it.
The data just isn’t there.
One of the things that I have seen and heard in talking to other data scientists, particularly Junior ones, is that there is a great reluctance.
For more for less experienced data scientists to say that they don’t know Say that there’s not enough data, there isn’t an answer to the problem, right? It’s a super uncomfortable answer, because people looking at you while you’re a data scientist, you should you should know everything about this.
The more experienced a data scientist is, the more likely it is like I said, Look, this is not a solvable problem, right? This is not there’s not enough data here, the data is wrong or it’s corrupted.
And until you fix those underlying infrastructure problems, you can’t solve this problem.
It’s just not possible.
It’s like, you want to make mac and cheese but you have no macaroni there.
There is no way for you to make mac and cheese without macaroni.
It’s just not possible.
And so those are the kinds of questions you want to ask in interviews.
They are scenario based they are.
There’s a lot of walk me through this explain how you do this.
What’s your approach? And when you start getting into what’s going to go wrong, That will be very telling about who that data scientist is.
If they are supremely overconfident in their answers, that’s actually a red flag, right? You would think, no, no, we want somebody who knows what they’re doing.
Well, yes, you do.
But a big part of data science and science in general is knowing that things are gonna go wrong a whole lot.
And, and being ready for that.
If you get somebody who says I’ve never run into any problems doing multiple regression, I’ve never run into any problem.
I’m so good.
I’m so good that I can build a clustering model with anything.
Doesn’t matter how good you are.
It matters how good the data is.
Right? So those are all the red flags, you’re looking for.
overconfidence, trying to bluff their way through something trying to as one of my martial arts teacher says reach for something that isn’t there all the time.
You want somebody who can help you plan who can help you do the data science and has enough experience that they know what’s going to go wrong in your data and help you solve it to the best of their abilities, or tell you what you’re going to need to do from a systems perspective or data perspective or even a people perspective to get the data you need in order to build good models.
So, if you have follow up questions on this topic, please leave them in the comments box below.
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