6 Tips to Manage and Scale a Data Science Team

by   |   June 15, 2017 5:30 am   |   0 Comments

Dr. Ken Sanford, U.S. lead Analytics Architect, Dataiku

Dr. Ken Sanford, U.S. lead Analytics Architect, Dataiku

As data science grows, companies are building dedicated teams to take on more complex projects. Data science teams are integral to many business’ success, but a functioning team won’t happen overnight. Only 27% of companies report that they successfully integrate new analytics talent with more traditional data workers.

I have observed that data science teams that try to scale too quickly or take on large, complicated projects immediately often find themselves faltering. The variety of skill sets, data knowledge and experience makes nurturing a data science team a difficult task for managers. Just pouring money into a team will not make it productive; instead, team nurturing should take place to foster growth.

Here are six tips for businesses ready to scale a data science team.

Tip #1: Make Sure Necessary Data is Available to Everyone

Though it may seem obvious, managers should start by making sure everyone on the team has access to data. That is, don’t arbitrarily restrict access. Teams need to be able to collaborate to complete projects efficiently. The team may realize they need to collect more data or different data, and then they can take the necessary steps to get this information. Finding out later on that a business isn’t collecting necessary data can create team chaos, so it’s important to keep this as a first step.

Tip #2: Understand the Business

A data scientist may be an expert in his or her field, but they are not going to help a team if they do not understand the company’s core value proposition. New team members must take the necessary time to learn what is important to the company and where data can help improve the company’s bottom line. Recent hires should understand what data is collected and why and how data science fits into the company’s overall objectives.

Tip #3: Start with One Task

When a data science team is growing, it’s important to focus on one task and work through it from start to finish. While so much of a business can improve through data, starting with one task allows the team to get a handle on working together, fostering trust and support internally. During this process, knowledge gaps can be addressed, and teams can work out any learning pains.

Tip #4: Hire Cautiously

Data science is a somewhat new industry. Many potential candidates have a range of skills, and resumes are often inflated and confusing. Hiring someone that does not fit with the team can cause chaos and a kink in the productivity chain. It’s important to use referrals to understand how a candidate works, what they are actually capable of and how well they would fit in with the company’s dynamic.

Tip #5: Select the Right Tools

As data demand grows, so does the need to hire quickly. Hiring a candidate that only codes in Python is no longer an option; instead, potential employees must be language- and tool-agnostic. Data team leaders should look into incorporating all of the available tools that can help the team reach their goals. The best way to remain tool-agnostic is to use a centralized platform that integrates with all of the tools available and encourages collaboration between team members with different backgrounds while maximizing productivity.

Tip #6: Learn New Skills

Data science is constantly changing, and with the introduction of artificial intelligence and machine learning capabilities, the skill set data scientists need will continually evolve. Once the data science team has become an agile, scalable team, managers should focus on the professional development of the staff. Certain team members should undergo mentorships with more senior team members to develop skills, and other employees can take courses or classes to become fluent in new data science best practices. As data cleaning and preparation becomes automated, it will be important to develop the skills of each team member.

Data Science Teams in the Future

This industry has seen accelerated growth in the past few years, so what a robust data science team will look like in the future is uncertain. It is also constantly evolving. When managing and scaling a data science team, managers must be sure to develop skills and deliver new creative challenges to keep the team learning. Receiving new tasks will keep the team nimble and productive, which is essential in this evolving industry. All team members should be aware of industry changes and developments and maintain a curious eye on the future of data science.

 

Dr. Ken Sanford is the U.S. lead Analytics Architect for Dataiku. He is a reformed academic economist who likes to empower customers to solve problems with data. In addition, Dr. Sanford teaches courses in Applied Forecasting, Stress testing and Big Data Tools for Economists at Boston College. He has a Ph.D. in Economics from the University of Kentucky in Lexington, and his work on price optimization has been published in peer-reviewed journals.

 

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