The Successful AI Team: How to Hire and Keep a Happy and Productive Team

Here's a walkthrough to help you build a successful AI Team.
By Claudia Virlanuta • Updated on Nov 17, 2022
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As AI rapidly grows in popularity, executives have a lot of technical questions about computing systems. AI can streamline your business and is now used for many mundane, time-using tasks that employees could use elsewhere in their jobs.


However, there are other important questions that need to be addressed regarding the human side of AI: How do you recruit members for your AI team? How do you attract potential employees? How do you manage your AI team once you have it? These are the questions that I will discuss throughout this article and I hope it will help you leverage AI in your company.

"A lot of companies are realizing the importance of how AI will impact their business. And they’re starting to build in-house AI teams."

- Andrew Ng, CEO at Landing AI and 


What Are the Best Recruitment Strategies?

Your AI team is powered by humans, and you should dedicate your efforts to find the best candidates in order to succeed with your AI strategy. If you are looking to recruit junior engineers to your AI team, bear in mind that the majority of potential talented candidates may lack experience. Choose to be open-minded and look for their willingness and enthusiasm to learn. Here are some ideas of how to find new junior engineers for your AI team:

First of all, you may want to cast a wide net when you search for people interested in your industry. Also, you may wish to exploit university partnerships by sponsoring student projects, or open internship programs. This way you have immediate access to fresh and ambitious minds. You can ask your current AI team (if you have one in place) to refer their friends. LinkedIn statistics show that over 80% of all jobs are filled through networking. Friends and colleagues of your current AI team are great considerations and will have the helpful addition of good rapport to fuel the team's interest. 

In addition, consider hosting a hackathon that is open to not only outsiders, but your engineers and data scientists. Hackathons are great events and attract talented people who are good candidates for AI teams. Hackathons can also promote communication and team bonding among teams. Competitive events like hackathons can be helpful team-building exercises for current and potential employees.


What Should You Look for in Potential AI Team Candidates? 

If you are looking to bring new scientists and researchers to your AI team, they should hold an advanced degree or substantial experience in statistical modeling and ML. You can recruit through a couple of ways: strategic networking, academic conferences, and blatant poaching.


A gold tip for you would be to sponsor conferences. Sponsoring academic conferences can help you build your brand reputation and will help your company to be more recognizable in your field. With popularity comes familiarity and more interest.

Another good idea is to host competitions on Kaggle or other similar platforms. Kaggle is an online community of data scientists and machine learning experts where individuals and teams can interact within environments. Competitions on platforms like Kaggle allow data scientists to grow and perfect their skills in a fun way. On why Kaggle offers competitions, Kaggle’s CTO said that some companies "have a really challenging use case and they want to put some of the best minds of the world on the problem, and to attack the use case in a very competitive setting."

Some really amazing things happened during Kaggle competitions. For instance, one competition consisted of developing automatic grading methods based on 28000 student-written essays. The top solutions were fully agreed by two human teachers. You will definitely find brilliant potential employees on platforms like Kaggle. 



What Can You Do if You Don’t Want to Hire Anyone New?

If you feel like you don’t have the energy and resources to find someone new, you can retrain your current engineers who are loyal and knowledgeable of your industry and domain. Provide them with resources to keep them updated on the latest tools so they can remain valuable to your company.

For example, grant your employees access to books and online courses. You can also pair up juniors with senior engineers or data scientists through mentorship programs. If you think the process of retraining your employees may be too lengthy, you can look up third-party solutions for your short and medium-term business needs. Consultants and training programs can help bring your AI team up to speed with the latest information without the hassle of hiring new people. 


Why Should People Want to be Employed by You?

Potential AI team members who are hunting for a job usually have multiple offers, so it’s not only about them impressing you but also you impressing them. Here's what AI candidates look for in a company:


1. Availability & Quality of Data

It is preferred that you have real-world, high-volume data that is already clean and annotated because AI staff really don’t enjoy the time-consuming data cleaning tasks.


2. A Challenge

Good candidates like to be challenged and the majority of them prefer working on multiple problems at once instead of focusing on one small part of a larger project.

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3. A Quality Team of Experts

Smart candidates want to outperform themselves and want to be surrounded by better professionals who can inspire them. So junior candidates may ask to be placed within a team of experts or senior employees in their field.  


4. Company Values with Impact

Show your candidates that their job will be meaningful and how they can contribute to the business's success. In the book The Why of Work by Dave and Wendy Ulrich, it is explained that people who find purpose in their jobs are happier, more creative and more engaged with their job.


What Do You Do Once You Hire Someone for Your AI Team?

Once you find and hire someone for your AI team, what's next? Though the process of hiring a new employee for your AI team is similar to any other hire, there's a few things to note to ensure your team is cohesive and productive from the get-go even with someone new. Here's a guide to the process you can put in practice for new a successful AI team member hire: onboarding, managing, and team-problem solving.


1. Onboarding

First of all, the onboarding should take place with the manager and that consists of going through an overview of the role, the expectations, and the projects. The manager needs to make clear who they are expected to interact with, how often interactions should take place, and who is responsible for the initiation of the interactions. 

Your manager shouldn’t just talk about these people as if they are imaginary but actual introductions should be made. Let your new team member know how feedback and review work within the current AI team, and what kind of team they will be part of (is it an independent or embedded AI team). Make sure you keep documentation of all this information because it’s a lot to wrap your head around as a newbie.

Next step is to make sure that human resources onboarding takes place, and once that is ready, inform the new employee about your equipment and communication means.

For example, let them know what computers and phones they can use, what is the available software and their purchasing limits, their work email, their database access, their system credentials, etc. Once you have bombarded them with all this information, give them some confidence and let them work on an initial small and concrete project that will let you know them better too.


2. Managing

When your new team member is not a newbie anymore, how do you actually manage your AI team?

First, I recommend you have weekly individual meetings and data science team meetings that include direct reports and cover project progress, goals, and any obstacles preventing progress. Have these formal meetings only as frequently as necessary so you don't burn out your AI team with constant check-ins.

Between these meetings monitor interactions through emails, Slack, or even meetings with external people. The secret to keeping your team management productive is to get your team unstuck fast in case they encounter any problems, and this way the distractions won’t happen too often and time will be used wisely.

As a manager, you need to stay on top of your team and be aware of everything that happens. Observe if your team members have too much or too little workload, and look out for interesting work and learning opportunities. Pay attention to whether or not your AI team members have enough autonomy, and keep an eye on your team culture to offer opportunities for team bonding. Grant your team opportunities for career growth like promotions, new available roles, and company visibility. Talk to your team to see if their personal life may affect their professional life. Be as friendly as possible and remember that they are not robots but people who go through events, milestones, birthdays, anniversaries - it helps if you remember these details! A pleasant work environment leads to faster and higher quality work progress in the long-run. 


3. Team-Problem Solving

Some often-encountered team issues are mainly caused by lack of interaction, too little empowerment, and no understanding. Let’s look at how each of these problems progresses and how you can solve them.

Usually, centralized AI teams go through a period of a lack of interaction for various reasons. For example, your data scientists may not know how and whom to contact, or which business stakeholders may be available for point-of-contact solutions. To solve any communication problems, make thorough introductions when you bring people on board. 

If there is a conflict arising with a stakeholder, make sure you are able to mediate it. Sometimes your team's workload may be too spare, other times their workload may be too heavy, and your data scientists don’t have time to contact their business partners. In situations like these examples, reassign projects or make things easier for your data scientists to work through the issue.

Embedded data scientists often receive no empowerment which in turn makes them feel isolated and excluded from decision making. No empowerment leads to employees lacking trust in their work. One solution for this is to support your data scientists by highlighting the importance of their job. Sometimes data scientists may feel like their business partners are ignoring their point of view, or that their business partners pressurize unrealistic expectations onto them. If this is the case, try and mediate the relationships and support your AI team. 

Lastly, team-work problems will appear when there is a lack of understanding if your AI team doesn’t understand the problem they need to work on or if their business partners do not understand the point of the AI team in the first place. To tackle these problems, clarify expectations and aims on both sides, and raise awareness of the AI team’s work throughout the organization. It's important to highlight the work your AI team is doing to provide adequate support for the projects they are in charge of. 



6 Best Practices to Use Within Your AI Team 

Forming and hiring a successful AI team depends heavily on some good practices I advise you consider. Applying these six important steps will keep your AI team happy and productive: 


1. Manage Projects with Time-Boxing

Time management can be a hang-up for projects both small and large. A successful way to manage a productive AI team is to reduce stress with time-boxing. Time-boxing is essentially setting a time-limit for steps of the project and then stepping back to assess the next step. Time-boxing allows for your AI team to see not only the intricacies of the project but the overall needs and problems that arise as they work. This minimizes general stress and prevents mistakes as the project progresses. 

To be sure everyone is onboard, decide limits for each time-box. Once a time-box is over, your team should come forward and assess their progress and future plans at that particular time point. Communication is key when it comes to AI teams and time management with time-boxing helps to encourage constant communication and successful project progression. 



2. Manage Project Expectations 

Sometimes an AI project is bigger and more complex than was expected. Your role as manager is to manage your team, but also to manage project expectations. It's good practice to promise achievements step-by-step and provide updates often. Make sure your projects have a clear ending, or you may to shelve projects until a later date. Trying to force a project to the end too soon will cause greater problems in the end - not just in the project itself, but among your AI team as well. Be sure to have everyone on the same page if you want to restart a shelved project. It will benefit the morale of the team and the quality of the project to have realistic expectations. 


3.  Advocate Team feedback

For a happy and productive AI Team it is important to not only make feedback obligatory for everyone, but to also remember that feedback tends to be more honest if it is anonymous. Go over this feedback throughout the monthly team meetings. For instance, you can create a shared spreadsheet for feedback. This can also help you understand what projects are not very productive. Knowing what works and what doesn't work is a key component of a successful team. 


4. Give Your Team Room to Focus

It is important that your AI team has at least one day a week of fully uninterrupted deep work, with limited emails, messages, and no distractions or pressure from people higher up. It may be easier to achieve this if you have an independent AI team that stays focused only on one project at a time. Providing focus-space will allow your AI team to not only be more productive, but it will also show that you trust your team's capabilities. When there are constant demands and distractions it builds stress and causes delays. Data science is concentrated work and AI projects need individual attention for proper quality. 


5. Keep Your AI Team Connected

Lack of communication between different team member leads can cause a lot of wasted time. If your AI team recreates tools and rediscovers processes that have already been discovered because there is no communication, your team is not really able to be successful in the long-run.  To avoid repetitive problems, encourage communication between all teams involved in the AI projects by organizing presentations where successful projects and failures are shared so that everyone can learn from mistakes and hang-ups.


6. Regular Rotation 

Blind spots in an AI team can happen when only one member of the team knows about a specific code, data pipeline, or process and everyone has to go to that one person. To avoid this bottle-neck and assure that knowledge is free-floating within the team, regularly rotate data engineers and data scientists between teams. This can also prevent boredom and keep your team members engaged with their work. 

Although AI is made to solve human problems, AI can often raises new challenges. When these challenges arise, what matters the most is your AI team’s makeup. When looking for new people for your team, remember that your network is your net-worth.

To find adequate hires for your AI team, you can search within your network and within your current employees’ networks, but don’t forget to look out for fresh talent from universities, hackathons, and competitions. And Don’t forget that bringing someone new to your team must be a symbiotic exchange - it’s not only you that should like them, they should like you, too.

Once you have your AI team members assembled, You have the next steps I provided and some issues I warned you about to prevent or solve problems that can arise in your team. As long as you keep in mind this guide for best practices within your AI team, you shouldn’t have much to worry about. Remember that your AI project's success is based on your team rather than any one individual! Keep your AI team happy and they will be productive. 



Claudia Virlanuta

CEO | Data Scientist

Claudia Virlanuta

Claudia is a data scientist, consultant and trainer. She is the CEO of Edlitera, a data science and machine learning training and consulting company helping teams and businesses futureproof themselves and turn their data into profits.

Before Edlitera, Claudia taught Computer Science at Harvard, and worked in biotech (Qiagen), marketing tech (ZoomInfo), and ecommerce (Wayfair). Claudia earned her degree in Economics from Yale, with a focus on Statistics and Computer Science.