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This blog is contributed by Burtch Works’ data science & analytics recruiting team

A few weeks ago, we spoke to our recruiting team that focuses on data science & analytics positions to ask about the latest news on WFH policies, full stack engineers, whether it makes more sense to be a generalist or a specialist, and whether you should use a job offer to negotiate a raise at your current employer.

 

Of course, one of the big topics right now is about is the future of work from home. Do you have any sense of what you’re hearing from our clients and other companies, about what their work from home and hybrid policies are going to be looking like for analytics and data science professionals?

It is a very relevant topic right now, and not just for our clients, but for the individuals that we speak to every day.

It’s been interesting that a number of data scientists and analytics professionals we’ve spoken to have actually said, “Hey, I’ve actually relocated, and I no longer live where I used to live. My employer doesn’t know, and I don’t plan on moving back, so I’m looking for a job right now in a new market, or a 100% remote opportunity.”

This might lead to an interesting situation in the next three to six months as organizations continue to bring their people back into the office and start making it mandatory. Based on conversations that we’ve had, it seems like there’s going to be a very limited number of organizations that are going to go back to 100%, five days expected to be in the office. The vast majority of organizations we hear from are at least looking at some sort of hybrid model, if they haven’t already adopted a 100% work from home attitude. And many of them are still going through that discussion, right now, to figure out what that’s going to look like and when that’s going to be put in place.

This situation is likely going to continue to evolve, so it could be that we have a different answer for you next year, but, for the moment, it looks like it’s going to be a high likelihood that almost all organizations are going to offer hybrid options because of the frenzy in the job market. In some cases, they may even be forced to go with a 100% remote option, depending on where they are and what talent they’re trying to reach.

Related links:

  1. Data Science & Marketing Research WFH Survey Results: Individual and Team Productivity
  2. Survey Results: WFH Preferences for Data Scientists, Data Engineers, & Researchers
Are you seeing any requests for a “full stack engineer”? A role combining data engineering, data science, and machine learning ops into one person, are you seeing that become more prevalent or no?

Not from what we’ve seen. There’s always going to be that kind of job, because if you’re in a startup situation, you’re expected to jump in, and be able to do everything, from the data munging through the modeling through the deployment. But what’s happened over the last five years or so is this real emergence of the data engineering role. And that person’s role is to get the data ready and work with the data scientist, but not necessarily be involved with the modeling aspect of things. And then, on the backend, once the models are developed, to make sure that the deployment of that goes well.

And the reason that encouraged data engineering to emerge as a discipline, is because employers don’t want data scientists spending all of their time doing data munging. And at one point, they used to say that was 80% of the job. So as data teams have expanded, we’re seen more roles take on different tasks so that it’s not just one person responsible for the entire process. You may see something like a “full stack engineer” who has to be able to do everything in smaller organizations, where an individual knows going into that role that they’re going to be asked to wear many hats, but in the mid-size to large-size organizations, they’re staying pretty separate.

Related links:

  1. Data Engineer vs. Data Scientist: What’s the Difference?
  2. Decoding Data Engineering Job Titles & Specializations

 

Do you think it makes more sense for an analytics or data science professional to be more of a generalist or a specialist, as far as their skill set?

Early on in someone’s career, the most important thing is to garner and gather as many skills as you possibly can. That’s going to allow you to have a broad sense, overall, of data science and analytics in a lot of different industries and organizations, and it will also allow you to understand what you enjoy, and where you really excel and can generate a lot of value for an organization.

Once someone gets to maybe around six or so years in their career, we typically see them start to specialize in a particular domain. Now, that’s not to say it’s impossible for someone to jump from one industry to another later in their career. But, typically organizations, from a technical leadership standpoint, if leadership is the route that you want to go, they’ll want somebody that has domain knowledge. They want someone with the ability to really hit the ground running and provide impact early on.

The more senior you go, you have to be able to point to some evidence that you have seen these sorts of business problems before. So our advice to any early career person is, make sure you build a very broad base of skills as much as you possibly can. Either by changing industries or changing applications, or whatever that might be, but do it early on, because the more experience you have, the more money you’re making.

A particular trend in Data Science, is that there’s been different tracks that people are starting to take. They will be become an expert in, let’s say, IOT, or Computer Vision, or next best action, or whatever that might be. But again, early on, get a broad base if you possibly can.

Related links:

  1. 12 Key Data Scientist Skills in 2021
  2. Beyond the Unicorn: 4 Developing Data Scientist Career Paths
Since a lot of people are looking to make career changes right now, what are your thoughts on getting job offers in order to negotiate your salary at your current company?

If you’re unhappy with your compensation, our advice is to go to your boss and tell them that you’re unhappy with your compensation. If you go and interview with other companies in the interest of getting job offer to negotiate, you’re sacrificing trust within your current organization, and that’s a tough thing to come by! You may end up burning bridges if you do something like this, and that trust is an even tougher thing to build back. It can also set you up for a toxic environment and toxic relationship, and it’s really, really difficult to rebuild and regain trust after it has been broken.

However, if you’re genuinely looking to make a change, and you want to at least have that conversation with your manager first, that’s the route to go. If your organization values your skillset and you believe that you provide a lot of value to them, that’s the more common approach. They might see that you can garner more out on the marketplace, especially if you have salary data to back that up, and that is much better than trying to get another job offer just as a negotiating tool.

Related links:

  1. 7 Tips for Negotiating Your Data Scientist Salary
  2. How Data Scientists & Analytics Pros Can Manage Multiple Job Offers

 

 

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