Things to stop to recruit top data talent.

Everything you’re doing to recruit top data talent is wrong

Wondering what the heck is going on with hiring these days? Why are there so many data-focused job openings and so few applicants? Why are your best candidates ghosting you?

The job of recruiting is really a job of marketing and sales. As the recruiter, you’re working to convince your customer (i.e. a candidate) to make the life changing decision to leave their current position and join your team. You need to find them (marketing) and sell them (sales). Many recruiting mistakes come from a failure to think about your open position as your product and the right candidate as your target customer.

Basic marketing training begins with a discussion of the 4 P’s of marketing: product, promotion, place, and price. In recruiting, these lay out as follows:

  • Product: The job you’re offering. In a crowded market, where everyone is trying to hire data talent, are you offering a desirable product? Is it something that a strong candidate is willing to “buy?” What can you do, what do you need to do, to make it super desirable?
  • Promotion: How do you let the right people know about the job you’re offering? Where will you promote it? How will you find active and passive candidates? If you put your job ad next to that of some other firm looking for talent, would yours stand out?
  • Place: In this context, place refers to where the job is located or, more specifically, where must the person you hire be located? Regardless of how you feel about remote work, many good candidates are looking for it. Can you support this? If not, can you provide some sort of hybrid arrangement?
  • Price: It’s helpful to think of the “price” in hiring as what it “costs” the candidate to take the job. Of course, this involves the pay they will earn but also the pay they are declining at other opportunities. However, price also includes career and personal trade offs as well, such as relocation.

And, let’s add one more P – process: Do you have a recruiting process that optimizes the efforts you make to attract talent?

So, with the 5 Ps as a framework, let’s look at some things you’ll want to stop doing if you want to hire great data talent.

Stop doing these things now if you want to hire great data talent

It’s no longer an “if you post it, they will come” hiring market. Attracting today’s best data talent means putting as much effort into wooing candidates as you expect those candidates to put into impressing you. To do this well, you need to stop relying on these outdated hiring practices:


Some employers, big and small, still seem to assume that a position at their company is desirable by default. It’s not. Data professionals who are considering a career change aren’t just looking for a different job. They are looking for a more meaningful way to spend a good chunk of their day. They want to put their talents to use to add value, and they want to be appropriately compensated and appreciated for that effort. But, they also want to be seen as a whole person who has a life beyond work, hobbies, personal goals, and a family and friends. 

Unemployment is low and you no longer hold all the cards. You’ll need to improve the “product” you’re offering in order to attract top data talent:

Stop creating jobs with unrealistic requirements and duties.
Job descriptions in the data sciences are known to be comically unrealistic. This seems to stem from one or more of the following reasons:

  • a lack of understanding about the field by the person writing the job description, 
  • an effort to cover all the bases because the role hasn’t really been well-defined, 
  • a not-so-subtle attempt to roll the work of several positions into one, and/or
  • an outdated belief that productivity is equivalent to regularly overworking. 

As you can imagine, “OMG! How can they expect one person to do all this?” isn’t really a motivating reaction to a job post. 

Stop ignoring the value of company culture.
It’s so often overlooked, but what it’s like working for an organization is just as important as the job to be done there. In fact, a toxic work environment is one of the top reasons given for finding a new job

When you neglect to share your company culture during the hiring process you’re sending a message that you only care about the work and not the people. What’s more, you’re not helping the candidate self-select if they would be a good fit in your organization.

A business that promotes a healthy and collaborative workplace sets themselves apart during the recruiting process. And this goes for roles that are remote or hybrid, too—the experience of working for your company still matters. 

Stop thinking you don’t have to take diversity, equity, and inclusion seriously.
Does your leadership team consist almost entirely of white men? Are you reluctant to sponsor visas? Are your holidays, benefits, and pay schedule stuck in the 1980s? Do you frequently find yourself rejecting candidates for being a poor “culture fit” without being able to describe exactly what you mean?

If you even answered “maybe” to any of these questions you may have work to do. Just putting a paragraph on your website about DEI with a suitably diverse team photo does not constitute organizational change. You can’t fake this, and it’s not going away.

Stop refusing to sponsor work visas.
Not only does this imply a lack of commitment to DEI (see above), but you are missing out on a huge pool of highly-qualified data talent.


How do you bring attention to an open data-focused role with your company? Of course job boards come into play here, but they are a tool not a solution. There’s a lot more to consider when you are “packaging” your open data role for promotion. Not only do you have to be wary of unrealistic laundry-list job descriptions (as we discussed above), you need to compete against all the other recruiters and employers out there trying to attract the same data talent you are.

Stop solely relying on job boards.
Today, hiring great talent requires recruiting, and just posting  your open data and analytics  job to a few online job boards doesn’t count. Anyone who is on LinkedIn is probably getting regular pings about open data-focused positions—are you actively pinging prospective candidates? This is where hiring expertise can be really helpful—an experienced data science recruiter will be able to read through the lines on a profile and resume and know whether a prospect is worth a ping.

Stop posting your job description as is.
To appeal to top data professionals, you need to sell the role. A bulleted list of duties and requirements with a promise of “competitive pay” is not going to convince highly-qualified candidates that a position on your data team is for them. You need to show prospective candidates that your company is a great place to work, and that the role will help them advance both career and personal goals. You need to explain the role, requirements, and responsibilities, but also answer the fundamental question job seekers have: what will my life be like if I get this job. 

Check out our in-depth blog post on how to write a great data science job description for lots of great tips. 

Stop being secretive about the pay.
Lots of businesses are reluctant to be upfront about the pay scale for data science-related positions. You may think of it as keeping your options open, but it frequently backfires and will turn off good candidates from applying. It sends a message that you are trying to hire at the lowest rate possible and that you may not have a good understanding of the value of the role. 

Searching for a job takes a lot of work. It’s a pretty big ask to expect top candidates to go through the process of applying and interviewing with no idea what you’re willing to pay. We’ve seen lots of great candidates bow out late in the interview process because they learned the pay was less than they were already making. In those cases, the unwillingness to disclose pay resulted in hours of wasted time. 

And, this goes for passive applicants as well. If you’re trying to woo a data scientist or data engineer away from their current job, you need to be able to tout pay as part of your pitch. 


In this context, we use place to refer to where the work is done for a data-focused role. And there’s a big change you need to make when it comes to the location for your open roles if you want to stay competitive in data science hiring: 

Stop mandating onsite work.
Many businesses are reluctant to fully embrace remote work now that pandemic restrictions have been lifted. While there are certainly pros to having teams work together physically, there are also significant benefits of remote work for employers. But probably the biggest downside to onsite only work is that it severely limits your pool of data professionals who are willing to apply to your job. 

One side issue to keep in mind, however, is that there are many people who actually want an office to go to. Perhaps they needn’t be there five days per week, but they want a place that’s away from home where they can concentrate on their work and meet their teammates. Thus, hybrid arrangements can be a valuable option.


Employment is an exchange. The employee uses their expertise and skill to help your company succeed, and in return they are compensated with a salary, benefits, and also the intangibles that they take home with them: stress level, flexibility, proximity to friends and loved ones, free time, etc. Too often, the hiring process is focused on the employer’s end of the equation and neglects to take into account the trade-offs to a candidate who is considering the role. 

To compete for top data talent, it’s vital to consider what is the costs to the candidate who chooses to work for you:

Stop trying to get the lowest rate possible for top data talent.
The most fundamental cost of a candidate taking a job with your company is going to be the pay. It’s rare that you’ll find someone who is willing to accept a pay cut, especially if the role requires more responsibility. Be upfront with your salary range and willing to negotiate with great candidates.

Stop offering the standard benefits package.
Great employees want more than just a paycheck from their job—they want perks. While healthcare, time off, and retirement are a good start, think of other ways you can sweeten the pot. (And no, we don’t mean ping pong tables in the break room, although those are nice, too). Consider things such as:

  • paid maternity leave and paternity leave,
  • continued education and training, 
  • healthy lifestyle with gym memberships or classes, and
  • flexible work hours, 

Stop eschewing remote or hybrid work options.
We discussed remote work above, but it factors in here as well. If you are locked into onsite work, you’re missing out on lots of great talent. But also consider how remote or hybrid work options can help make the “cost” of taking your job more appealing to data science candidates:

  • little to no commute,
  • no expensive move or need to uproot families, 
  • freedom to work as they choose, or
  • flexibility for parents.


The traditional hiring process tends to be very impersonal. The onus is on the candidate to jump through the right hoops, which often leaves them alone in one of the most important decisions in their life. The one-sided view that the employer is paying so the job seeker must comply is outdated. You are asking an individual to invest a significant portion of their waking life for the betterment of your company. It only makes sense that good customer service has to go both ways! 

Make these changes to your overall hiring process to keep your best candidates engaged in the interview process:

Stop using HR to completely handle hiring for data-focused talent.
Not only are HR departments already overburdened, they also know little to nothing about the data sciences. Data team managers are the ones with the need and the knowledge, not to mention the people who will be working directly with the new hire. Hiring done by the people the candidate will actually be working with is vital to finding the best fit of both skills and personality.

Stop over-relying on applicant tracking systems (ATS) to screen data science candidates.ATSs are a great place to collect, store, and track job applications, but they also come with some helpful features that tend to get abused—namely, the ability to screen resumes based on automated keyword matches. While this may seem like a great time saver, there are a couple big problems that we see quite frequently:

  • Savvy candidates (especially those who work in data science and technology fields) know all too well that ATSs will scan their resume for keywords that match the requirements in the job description. Many will optimize their resume to game the system, rather than focus on the substance of their skills and experience. As a result, you will end up with a lot of unqualified candidates that make it through an ATS screening. 
  • The flip side to this is that many great candidates will be excluded from your pool if they aren’t thinking of their resume in such transactional terms. We find that the more job experience data professionals have, the more likely they are to rely on the actual substance of their resume to sell them. This nuance can be completely lost if you only rely on keyword matching. 

Stop expecting the entire hiring process to run only on your schedule.
We see companies lose out on exceptional data analysts, data engineers, and data scientists, because they couldn’t keep the process moving. This means doing things like:

  • not keeping candidates informed, 
  • taking too long to make decisions,
  • requiring arduous skills testing, and
  • assuming a candidate will wait around for you.

Here’s a hint: if someone is interviewing with your company, they are interviewing with others. This goes not only for active applicants, but also those who’ve been recruited. Skilled data professionals are usually being wooed by more than one recruiter at any given time. 

Remember the golden rule
In the end, candidates are just people. Treat them the way you’d like to be treated, with respect. One area we hear about every day is companies ghosting candidates. If you’ve started the recruiting process with a candidate but then decided not to advance them, do them the courtesy of letting them know. Ghosting candidates not only leaves them hanging, but it also reflects poorly on your company. Reputation always counts, especially in a competitive market.

Now get out there and hire the best candidates for your open data roles. 

Like it or not, the hiring market has shifted. And it seems highly unlikely that it will return to pre-Great Resignation normals. Successful employers are the ones who are able to adapt to a new normal (geez, I thought we were done with that term…) in employee expectations. The bottom line is, job seekers don’t want a job, they want a framework to use their skills and talents in a meaningful way. They want to be valued for their contributions. They want a means to secure their current and future stability. And they want to have a balanced life that includes family and personal interests. 

Need help recruiting top-notch data talent?

Dataspace is different because we know data. You’ll save yourself time and get better candidates to review. Contact us to book an informational meeting with our recruiters. 

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