Monina asks, “Not every marketer can (or wants to) be the data scientist for their org. What should we look for when hiring an FTE or partner?”
This is a critical question because there’s a significant shortage of trained data scientists. Those who exist and are qualified are “reassuringly expensive”. Thus, be on the lookout to evaluate the 6 skill areas that a data science individual or agency must have: coding, stats & math, data engineering, domain expertise, business expertise, and science expertise. If hiring, you may need to hire a team rather than a single individual.
Beware of “crash course data scientists”! These folks generally have only one of the six skill sets and limited or no practical experience. Remember that expertise is all about knowing what’s going to go wrong – anyone can do things well when everything is perfect.
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
- Got a question for You Ask, I’ll Answer? Submit it here!
- Subscribe to my weekly newsletter for more useful marketing tips.
- Find older episodes of You Ask, I Answer on my YouTube channel.
- Need help with your company’s data and analytics? Let me know!
- Join my free Slack group for marketers interested in analytics!
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 menina asks, not every marketer can or wants to be the data scientist for their organization.
What should we look for when hiring an FTP or partner? This is a critical question because there is a significant shortage of qualified data scientists, right those those who exist and are qualified are in the words of my friend Tom Webster, reassuringly expensive.
The last public figure I saw, there was something like 10,000 qualified data scientists with four years of experience or more in America, and something like 15,000 marine biologists so more people know about whales than data science.
So what should we be looking for? Well remember, and we’ll bring this up here.
data scientists have these four skill sets right business skills side skills, mathematical skills and technological skills.
Here’s the thing.
Because of the talent shortage, the likelihood that you can find someone who is proficient in all of these is pretty rare.
They are, you know, unicorns in a lot of ways.
And so there are, well, it’s, it’s more likely that you’re going to have to either find someone who has a strength in a couple areas and and is okay in a couple areas.
Or, more realistically, either hire an agency partner, or hire a couple of people or maybe even a team of people, depending on your budget, who can bring the different skill sets to bear.
So this is the six sets of skills that you would be hiring for.
Presumably, you would have someone who’s a business expert in your line of business, and a domain expert.
Someone who specializes in what is your company does so those two should Are you baked in if you don’t have those in your business? That’s a bigger problem than a data science problem.
That’s a fundamental business problem.
Data engineering is a set of skills where have someone who can work with where data is stored, how to store it, how to make it accessible, how to make it reliable.
Those are critical skills.
And that’s an area where many data scientists actually do fall pretty short.
Because you need a fairly strong technology background for that that’s working with SQL databases, and no SQL databases, graphing databases, cloud all the different cloud services that are out there, and data engineering is something where that person doesn’t have to be on a data science team they can be part of the IT team and then this be a resources available to be shared.
Because those skills are highly transferable to and from it, and are necessary in in most modern organizations if you if you’re storing data.
You either have or should have a data engineer or someone with data engineering skills.
That leaves the three primary areas for data scientists stats and math expertise, statistics, probability, linear algebra, some calculus, being able to understand the different theories and the different techniques and what they do and what they mean is critical.
What they mean part is critical because it’s easy to learn, you know, a particular mathematical technique with the harder part is pairing it with that either domain or business expertise to say this is why you would use this technique in this situation.
You have the coding, of course in languages like our Python, SPSS, whatever the statistical language of your choices to do data science work, and then that scientific mindset that ability to adhere to the scientific method just to set up well controlled tests and experiments, the ability to understand that certain types of validity to understand like yet this is a valid experiment or no this is not a valid result being able to interpret data? Well, from a reproducibility perspective, those are the six core skill areas that you would be hiring for.
Again, it’s difficult to find this all inside of one person that does it equally well, everyone out there is going to have different levels of skill.
For example, I am weaker on the stats and math than I am on the coding.
I’m a better coder than I am a stats person, I can do most stats, and I’m familiar with a good number of the techniques, but I know that academically that’s my weakest spot, data engineering, also not no problem.
I know other folks who are phenomenal statisticians, coatings, not their thing.
And they have many, many tools, you know, like at the SPSS modeler that allow you to circumvent that to some degree.
But if you’re trying to work with the latest and greatest, for example, in neural networks, you do need coding ability.
One thing to be careful of when we’re talking about hiring, you will need help interviewing if you don’t have some background in these areas, too, because there are a lot of folks out there who did these Crash Course, data science programs, right? Learn data science in six weeks.
It’s like saying learn surgery in six weeks.
I mean, yeah, you could probably become minimally competent at something, but it’s not somebody I want working on me.
And there’s a lot of those folks who are who are proclaiming themselves leaving themselves data scientists after going through one of these courses, you will need help interviewing to know what to ask people in each of these domain areas to be able to ask them challenging questions.
behavioral questions, situational questions, hey, you have this type of survey data that came back How would you handle it knowing that you can’t go back and redo a survey or we have this data from social media? How would you interpret it and be able to assess the validity of their answers? That’s Going to be the hardest part of hiring, I would recommend, in a case like that, find a reputable data science agency and asked to partner with them just on the help us hire somebody who’s qualified front.
Again, be real careful with those Crash Course folks.
Generally speaking, somebody comes out of a data science Crash Course is going to have one, maybe one and a half of these the six skill sets, and more importantly, limited or no practical experience, it’s super easy to go through a training course right and follow the instructions, take the datasets that have provided the materials and and work with them.
And that’s okay, right that you need to start somewhere.
But if you’re hiring for your company, you want someone with experience because expertise in data science, like all forms of expertise is less about knowing how to do something when everything is great, and the data is perfect, which I never Isn’t reality, and much more about knowing what’s going to go wrong when you look at a data set and go, Oh, I know exactly what’s going to screw up on this set.
Right and, and exactly what you need to do to mitigate that and still be able to get your work done, it will never be perfect.
But knowing like, Okay, what level of risk is acceptable here? example, you have a data set with, you know, 25% missing data, what techniques do you use to manage that? And part of that question is that experience teaches you is, what’s the level of risk? If you’re doing if you have this data and you’re doing it for like a marketing white paper, the risk is pretty much zero.
So you can do something like predictive amputation, right, there’s you’re not going to kill anybody.
On the other hand, if it’s a if it’s a trial pharmaceutical that’s going to be put into human trials.
You don’t you don’t do the thoughts fill in the missing data without without a best guess algorithm, because you might actually kill people.
So that level of risk is substantially higher.
So that’s where those funds Crash Course, folks, they Yes, start them out as like a junior analyst, let them get their skills, but don’t hire them as a data scientist expect to get the same level of results that you would get from somebody who has the scars and the and the stories in order to deal with the things that are going to go wrong.
So the really important question, how do you hire and bring on these folks, like you’re looking at an agency again, assess these things, asking the agency Hey, how would you handle this? Be very careful if an agency proclaims they have data science expertise, and you never actually get to talk to the data scientist.
It’s like, you know, hiring a technology company you don’t ever talked to the engineers.
always talk to the engineers always talk to the scientists, they may not be the most personable people.
But you’ll get a sense very quickly of how good they are or not in reality.
So really good question.
If you have follow up questions, leave them below.
Subscribe to the YouTube channel and the newsletter I’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems.
This is trusted TrustInsights.ai AI today and let us know how we can help you
You might also enjoy:
- How to Set Your Public Speaking Fee
- How To Set Your Consulting Billing Rates and Fees
- Best Practices for Public Speaking Pages
- Transforming People, Process, and Technology, Part 1
- The Evolution of the Data-Driven Company
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers