Need to Expand Your Data Team? Here’s What’s in Store.

Leaders from Prove and BombBomb share the lessons they learned from scaling their teams quickly.

Written by Eva Roethler
Published on Sep. 23, 2021
Need to Expand Your Data Team? Here’s What’s in Store.
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As the importance of big data increases for businesses of all types, the professionals who handle it continue to be in high demand. 

The U.S. Bureau of Labor Statistics recently predicted that the data science field will grow about 28 percent through 2026. And according to a survey by Burtch Works, 73 percent of data and analytics teams planned to hire in the first half of 2021, with 81 percent making similar moves in the latter half of the year. Indeed there are currently more than 10,000 job listings in data and analytics categories across Built In. 

On the ground, these statistics translate to hiring managers scaling up their data teams, searching for candidates who round out team expertise and add to the culture. 

Finding the right fit is imperative to scaling teams successfully. To see what this looks like in action, we talked to local leaders for insight into what they look for, how they’ve grown and the lessons they have learned along the way. 

 

Manish Dalwani
Director of Data Science • Prove

Prove provides identity authentication technology. 
 

When it comes to scaling your data team, what are the most important hiring considerations and why?

We now live in the world of the digital economy, and Prove specializes in digital authentication and mitigating fraud. The data science team at Prove handles several different compartments that include proof of concepts working with the sales team, customer success-related projects working with account managers, and importantly, research and development working with the product owners and managers to ensure that the products stay state of the art and cutting edge. 

When it comes to the data science team, the most important hiring considerations are based on meeting the expectations of the business. As the company scales, it’s important to understand the current skill set of the team, distribution of workload, estimating the bandwidth available and identifying the possible gaps that need to be filled. It helps to focus on the exact skills and technical acumen needed to hire a new team member who can fulfill the expectations and fit the business needs. I try to identify energetic individuals who have the passion, drive, motivation and hunger to work in a high-growth company. We’re looking for great talent from all over the world.
 

When it comes to the data science team, the most important hiring considerations are based on meeting the expectations of the business.” 

 

On the technical side, what steps have you taken to make sure your tools, systems, processes, and workflows are set up to scale successfully alongside your team?

The heightened demand for support from data science teams requires constant organic evolution of the tools, systems, processes and workflows that will enable expansion. A few examples include infrastructure development, pipeline management and optimization to handle big data and machine learning, automation of scripts, and a range of processes. This involves working closely with the data engineering and data operations teams to identify the bottlenecks and prioritize key needs. 

Our continual efforts to improve automation help add efficiency and allow the appropriate use of time to be spent by our data science teams on projects. To do so, it’s imperative we find the right balance between the technical needs of the data science team and the pipelines or architecture — that is a continuously evolving goal for a growth company like Prove. But it also means lots of new opportunities for hiring and expansion.
 

What’s the most important lesson you’ve learned as you’ve scaled your data team, and how do you continue to apply that lesson?

Onboarding is extremely important for new team members to have a deep understanding of Prove’s products. Of course, the technical acumen that they possess helps them to get familiar with Prove scripts, pipelines and processes. But in order to be a successful data analyst or scientist, it is essential to know the nuances of how the product functions. We make sure that our onboarding for the data science team is as solid and thorough as it can possibly be.

Another lesson I’ve learned is the importance of time and people management — with bigger teams comes more responsibilities. As we continue to grow, there is a constant need to mentor and consult on projects to support success for the collective. Experience is the best teacher, and I’ve tried to apply my learnings in my daily work and continue to learn and share my knowledge with the awesome team we’ve been building out. 

 

 

Danae Whitten
Director of Data & Analytics • BombBomb

 BombBomb is a B2B video messaging platform. 

 

When it comes to scaling your data team, what are the most important hiring considerations and why?

Within the data team, we emphasize culture and team fit as well as technical expertise and ensuring each member brings a unique thought process to our constant problem-solving work. As a team whose success is predicated on technical expertise and enhanced by all those using their own experience and ideas to solve complex data problems, a technical skill set along with ensuring the individual thrives in our team is paramount in all the data roles.

We emphasize culture and team fit as well as technical expertise, ensuring each member brings a unique thought process to our constant problem-solving work.”

 

On the technical side, what steps have you taken to make sure your tools, systems, processes and workflows are set up to scale successfully alongside your team?

Given the dynamic nature of technology, it’s important for us to make sure our tools are flexible enough to support new types of data but remain reliable and efficient. Early on we made investments in data tools that allow for long-term sustainability and fast growth. Using tools like AWS, Tableau and cloud-based ETL tools allow our data team to access data from all different areas of the business, centralize the data, and make data actionable and attainable by all departments of BombBomb.
 

What’s the most important lesson you’ve learned as you’ve scaled your data team, and how do you continue to apply that lesson?

It’s important to communicate our mission and fully understand the business problems we are striving to solve. We have found success by doing quarterly meet-ups where we start by reviewing our mission, talking about what is working for our team, and then addressing areas of opportunity for growth. These meetups not only build trust within the whole team but allow for everyone on the team to engage in the “why” of their roles and reflect on what we have accomplished and areas of potential growth and learning.

 

 

Responses have been edited for length and clarity. Images via listed companies and Shutterstock.

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