Why a Data Science Team is Advantageous

Problem

As companies are turning to data to introduce more efficient products and processes, there has never been a better time to enter into the field of data science. I currently work for a company that relies on predictive modeling. I work closely with intelligent, knowledgeable, and infectiously passionate people up and down our technology stack and across business units. My team and I are armed and ready to offer insights into any project the company needs to tackle. Here are examples of those insights, as well as the types of decisions that are made thanks to analysis provided by my team.

Actions taken

• Fairness is one of our core values due to the fact that one of our primary goals is to provide consumers with access to cars they want at great prices with no long-term commitment. Thus, we've developed models to fill our marketplace with a wide variety of vehicles at fair monthly payments to serve a broad range of customers. Our predictive modeling in vehicle value and consumer behavior are some of our major efforts driving this.
• We aim to provide a seamless and simple experience while acquiring a vehicle. Data science and analytics help us do so by deciding how we can shape our customers' experience in making onboarding and shopping as intuitive as possible using data-driven solutions.

Lessons learned

• Data science teams should be constantly learning and have project ownership. This enables data scientists to develop into what I describe as "full-stack data scientists" whose skills can cover data warehouse and ETL engineering, analytics and machine-learning model research and development, distributed learning platform engineering and production service deployment.
• Encourage your leadership team to create avenues for the free exchange of ideas and knowledge throughout the company.

Source: https://www.builtinla.com/2017/11/29/inside-look-los-angeles-data-science-teams