back
Your free e-book!
See when it is not worth using Scrum.
"Why Scrum Doesn't Work" Download

HOW TO: Build a Brand New Data Science Team and Avoid a Failure

This is a case where either you can expand your current development team with a few additional AI engineers who have extensive Data Science experience. You can also hire an external partner who will guide you through the entire process of building such a team from A to Z. Which option is better? Where will you create more savings and bring a better AI development? Can these solutions be combined? You will find answers to all these questions in the text below.

Why do you even need a data science team?

Every self-respecting company with a proven AI solution should have at least one developer who understands the concept of data science. In the case of a large enterprise, a data team of experts is necessary, who will be dealing with the selection of data on an ongoing basis, which in further stages of software development will be implemented with AI tech.

In fact, it means that building such a team is necessary but also very expensive for the company. Developers and engineers who have experience in data science earn much more than standard developers. Finding such experts is difficult because they are very selective about job offers – sometimes it takes long months to find such a developer at a reasonable price.

Who will build a data science team for you?

In most cases, it is from the experience of software development agencies that the development of such a team takes place in an organic manner. 

Example: Your company has decided to implement a solution that combines machine learning and artificial intelligence technologies, so you employ one or a maximum of two engineers and they implement the solution. Along with the development of your company and the growing popularity of your solution, you employ new data science engineers. This method is great because it does not expose you to huge costs, and you are still able to expand your project or make a new one from scratch.

There is also an answer to one of the questions that appeared at the top here. Yes, you can apply a two-fold solution to your new data science team. You do not have to look for a data science engineer yourself, you can sign a tech-partnership with a company that will support you from the very beginning. This means that he will build a relevant team of developers who will create your project. This is the best solution, because this way you get remote developers that you don’t need to have in your office and you still save money.

What to avoid when building a data science team?

  1. Avoid building a large team

This is the easiest, but from our experience, the most common mistake to make. Creating an entire team of data science developers at once is very costly and risky. Implementing such a team into your current development team may result in too much knowledge that all data science developers, not a single person, will have to acquire. While extending the team by two people is fine, a 5+ person team is very risky and you should avoid it.

  1. Employment of poorly experienced developers

There are a lot of freelancers on the market who seem to have a lot of experience in data science, but the truth may be completely different. Therefore, it is worth employing only developers with proven professional experience. Finding such experienced and talented developers is very difficult, but not impossible. However, finding such a person alone is very difficult, not to mention complimenting the entire team.

  1. Lack of the transparency

At this point, it is worth considering the detail of cooperation between a data engineer and a developer in your team. All programming code, ideas, built figures or even scratched data – must be available to the data engineer. Skillful communication will be a mutual benefit because the developers can learn new things and the data engineer can easily analyze the data and develop your AI project.

Concrete data analysis and experiments

Anyone with basic technical knowledge realizes that in the case of artificial intelligence, the biggest unknown, but also the most important thing, is data. If their number is large, they are properly selected and they are of great quality, then the AI ​​project may be successful and the data engineer will have a simpler job.

Establishing a certain amount of repeatability when structuring the data is crucial and often requires hundreds of hours of work. Once the data is organized in a structure, the next step after experimenting is automation. Therefore, underestimating when creating data engineers, which team often takes into account such roles as data analysts, is highly risky for the company.

Business context in combination with data science

This is an extremely important thread, because it often happens that data science has nothing to do with the business model. This is an extremely important thread, because it often happens that data science has nothing to do with the business model. Often, business requires specific, developed and backed up data that are able to help make business decisions that affect the company’s development – in the initial stages it is impossible. 

Because in order for artificial intelligence or even a data science team to understand the specifics of business enough to collect the necessary data – time and in-depth delving into the company’s processes and needs are needed.


cto - Chris Gibas

Free 30-minute consultation with our CTO

Chris Gibas - our CTO will be happy to discuss your project! Let's talk!

More blog posts
10 free tools to build an MVP
Great Place to Work: We’ve been awarded!
How to prioritize features in MVP?
How Much Does It Cost To Develop Custom Software – A Short Guide
Get a free estimation
Need a successful project?