As your business begins to scale, you may find yourself in need of additional data science resources, and team members. This isn’t always an easy process — how do you know who to trust with your company’s information and network? And how can you be sure that they will be the right fit for your organization?

In this article, we’ll outline best practices for hiring the right data science team to complement your operations. We'll also help you understand what to look for, and how to make sure that your new data science team can help contribute to your overall goals.

Why do I need a data science team?

The first step in hiring a data science team is to identify your internal data-related needs. Do you need someone to manage your company’s data? Do you need help with migrating your information from one data collection platform to another? Do you also need help in collecting that data, or in applying strategies that help you derive meaningful conclusions from the data you collect?

Do you need a specialist, or specialists, who can manage or optimize, your entire data science process for you?

If you're expanding your business, changing your business model, or simply attempting to learn more from your business practices or customer behaviors, you might need a more hands-on approach to data science. And if you’re not sure where to turn for your data science needs, you might need a data science partner that can help diagnose your data needs before helping to satisfy them.

Understanding your data science needs will help you identify the right team members for the job.

How do I form the right data science team?

Let's talk about how to form a data science workforce that improves your organization’s use of the information you collect.

When determining which type of data science team you’re assembling, there are a few important steps to keep in mind.

1. Identify your data science needs

Before you begin assembling your data team, it’s important that your entire organization understands its data science needs. For example, small organizations might not need an entire data science team. Instead, a single data scientist or data science specialist might do the trick.

Take the time to connect with your team and identify the preferred scope of your data science needs. This is also a conversation you can have with prospective data strategists, to help gauge your own needs and their aptitude for the projects you’re working on.

Once you've identified your data-related needs, you can start putting together your data science team.

2. Identify the data science skills you’re looking for

The next step is to identify the skills your data science team will need. Speak with your current marketing team or communications team to identify the specific skills to prioritize in data science candidates.

Some of the data science skills your team might need include:

  • Machine learning
  • Deep learning
  • Programming
  • Data visualization
  • Mathematics and statistics

Once you understand the skillsets your data projects might require, you can begin assessing how many staff members you’ll need to recruit. 

3. Identify the size of the data science team your need

The size of the data science team you need will depend on the scope of your project. If you have a smaller amount of organized data, or you’re only seeking trends from a consolidated number of data points, you may only need help creating a limited data science team.

However, if you're looking to build algorithms and study complicated data sets, or if you need help implementing data science systems from scratch, a larger data science team might be in order.

When assembling your data science team, it’s also important to ensure that all important data science skills are accounted for. In other words, you’ll want to make sure that the data science team you assemble can handle a wide range of data functions, to satisfy the entire scope of your data-related needs.

For example, you might need someone who's an expert in algorithm engineering, someone who's an expert in programming and someone who's an expert in data aggregation. You might also need a data analyst or two if you're planning on deriving even deeper conclusions from the information you collect. These individuals will also need to understand how to effectively work together to execute efficient data processes, without compromising your productivity.

How do I find the right data candidates?

It’s time to start looking for data science candidates. Sometimes, the right place to begin your search is your own workforce. See if there are any employees on your team who have the skills you're looking for. If not, you can begin looking for candidates outside of your organization.

Here are a few places where you might find high-quality data science professionals:

  • Online job boards
  • Data science and data marketing conferences
  • Recruiting firms
  • Professional social media platforms

Once you've found a few data team candidates, consider asking them a few questions to better determine their aptitude for your cloud computing team.

These questions might include:

  • What experience do you have with data science?
  • What data science platforms are you familiar with?
  • What data projects have you worked on in the past?
  • How would you approach a project that requires data science?
  • What do you think are the benefits of data science?
  • How might you approach a new data science project for an organization?

After asking these questions, you should have a good idea as to whether or not the candidate is right for the job. Remember, it's important to find someone who is not only qualified, but also a good fit for your company and your existing marketing team.

How to avoid hiring the wrong data science team

Now that you know how to hire the right data science team, let's take a look at how you can avoid hiring the wrong one.

There are a few things to watch out for when interviewing candidates:

  • Make sure candidates are familiar with the data science platform you're using. If they're not, they'll likely need to be trained, which can be costly and time-consuming.
  • Find out if candidates have any industry experience. While data science is a relatively new field, it's still important to find someone who knows what they're doing.
  • Ask about a candidate’s availability. If they're only available for a limited number of hours, or if they're already working on another project, they might not be the best fit for your needs.
  • Finally, make sure you're comfortable with a candidate’s communication style. You'll be working with them closely, so it's important that you're able to communicate effectively.

Asking the right questions in an interview is as important as the candidates you choose to interview in the first place. The required skills can be assessed from a résumé, but an effective interview is critical to understanding if a potential hire will fit effectively into your company culture.

Connect with our Admissions team today to get started putting data science best practices to use.