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How to Build a Top Level Data Science Team

Data Science Team

Businesses today need to do more than merely acknowledge big data. They need to embrace data and analytics and make them an integral part of their company. Of course, this will require building a quality team of data scientists to handle the data and analytics for the company. Choosing the right members for the team can be difficult, mainly because the field is so new and many companies are still trying to learn exactly what a good data scientist should offer. Putting together an entire team has the potential to be more difficult. The following information should help to make the process easier.

The Right People

What roles need to be filled for a data science team? You will need to have data scientists who can work on large datasets and who understand the theory behind the science. They should also be capable of developing predictive models. Data engineers and data software developers are important, too. They need to understand architecture, infrastructure, and distributed programming.

Some of the other roles to fill in a data science team include the data solutions architect, data platform administrator, full-stack developer, and designer. Those companies that have teams focusing on building data products will also likely want to have a product manager on the team. If you have a team that has a lot of skill but that is low on real world experience, you may also want to have a project manager on the team. They can help to keep the team on the right track.

The Right Processes

When it comes to the processes, the key thing to remember with data science is agility. The team needs the ability to access and watch data in real time. It is important to do more than just measure the data. The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes. They should not be handcuffed to a slow and tedious process, as this will limit effectiveness. Ideally, the team will have a good working relationship with heads of other departments, so they work together in agile multi-disciplinary teams to make the best use of the data gathered.

The Platform

When building a data science team, it is also important to consider the platform your company is using for the process. A range of options are available including Hadoop and Spark. Hadoop is the market leader when it comes to big data technology, and it is an essential skill for all professionals who get into the field. When it comes to real-time processing, Spark is becoming increasingly important. It is a good idea to have all the big data team members skilled with Spark, too.

If you have people on the team that do not have these skills and that do not know how to use the various platforms, it is important they learn. Certification courses can be a great option for teaching the additional skills needed, and to get everyone on the team on the same page.

Some of the other platforms to consider include the Google Cloud Platform, and business analytics using Excel. Understanding the fundamentals of these systems can provide a good overall foundation for the team members.

Take Your Time

When you are creating a data science team for the company, you do not want to rush and choose the wrong people and platforms or not have quality processes in place. Take your time to create a team that will provide your company with the quality and professionalism it needs.

Originally published Feb 3rd via LinkedIn

By Ronald van Loon

Ronald van Loon

Ronald has been recognized as one of the top 10 Global Big Data, IoT, Data Science, Predictive Analytics, Business Intelligence Influencer by Onalytica, Data Science Central, Klout, Dataconomy, is author for leading Big Data sites like The Economist, Datafloq and Data Science Central.

Ronald has recently joined the CloudTweaks syndication influencer program. You will now be able to read many of Ronald's syndicated articles here.

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