In this special guest feature, Deep Varma, VP of Engineering at Trulia discusses how managers can best support their new team of data scientists, bridge the gap between data science and product teams, and what to look out for during the interview process. Deep leads all engineering functions across the Trulia business as Vice President of Engineering. This includes the front-end teams responsible for building Trulia’s consumer products for mobile, web, and email and other communications, as well as the back-end teams focused on analytics, data science, data warehouse, listings and public records acquisition, personalization, search and QA. During his more than 20 years of experience, Deep has focused on building large-scale distributed web, mobile and data platforms with IBM, ABB, Yahoo! and two successful startups.
Data scientists are the new software engineers. Beginning in the 1980s, demand for software engineers spiked as computing systems became widely accessible. Now as data collection methods and Artificial Intelligence capabilities continue to expand, data scientists are hired to help companies explore and develop new technologies that can help them stay relevant and competitive. But not all companies have the foundation to support this rising group of mission critical professionals. Don’t get left behind; here’s what you need to know to set your company up for success.
I . Assess whether you even need data scientists
Needs vary depending on the stage of your company, its business goals, and products and services. While data science is valuable, not all companies are ready to invest in building up a data science team.
To evaluate if you need to hire data science professionals, first and foremost, determine if your company has business problems that they can help solve. The underlying problem that usually calls for hiring data scientists is having access to lots of raw data that have gone unanalyzed, such as user data. Machine learning and data science go hand-in-hand in this situation; machine learning is the key to unlocking the data’s value and data scientists use this technology to have a better handle on it all.
Second, figure out if your organization has enough of the right data. For example, if your company has a CRM system in place, the types of data that it should collect include customer preferences, purchasing habits, demographic info, and any other information that will help the sales team ßßmake the right call. It’s one thing to have big data, but another to have meaningful enough data that you can extract insights from. Some companies will struggle with this more than others. Niche verticals, like real estate, might benefit more from building their own deep data sets versus outsourcing data from third-party providers like Google.
II. Get leadership’s buy-in
If you determine you have a need to hire data scientists, you must lay the groundwork by first getting buy-in from your leadership team. Use proofs of concept – identify issues and prove they can be solved with data science. In real estate, for example, our team at Trulia often faces the challenge of pulling useful insight from property images. Our data science team has really helped us here. They have determined the best modeling techniques to identify objects within an image, the scene type of an image, and their attractiveness.
III. Structure for growth
Once you’re ready to ramp up and grow your data science team, you’ll need to think critically about organizational structure and how the team interacts with other departments within the company.
A growing data science team needs the space to get lost in the data. This calls for a flat organizational structure that will enable the team to act as explorers; a hierarchical structure will only put them in a box with little room for innovation. A flat structure will also allow for better teamwork within the data science team since it encourages a free exchange of ideas.
Teamwork, both within the data science team and with other groups, is a key to growth. To successfully scale and build great products, there needs to be close communication with engineers and the product management team. When the organizational structure and teamwork is solid, your data science team is poised to operate like a well-oiled machine.
At a time where data is plentiful and AI capabilities are growing, it’s important to assess whether your business is ready to bring on data scientists, understand what’s needed to set a strong foundation for this group of talent, and how to scale for growth. Unlocking the value of your company’s data will be instrumental in taking your business to the next level – and your data scientists will play a key role in that.
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