TEPM 046: Innovative Collaboration Strategies for Data Science and Engineering Teams

In this episode, I talk with Akshay Swaminathan, head of data science at Cerebral, about how data science and engineering teams work together to integrate customer perspectives. He also talks about the importance of stakeholder and clinician support, addressing valuable business problems, and ensuring users have the tech to maintain the models. ***The video version of this episode can be viewed here.*** Engineering Quotes: Here Are Some of the Questions I Ask Akshay: How does collaboration between data science and engineering affect integrating customer perspectives into data projects? How do engineering practices ensure data solutions meet customer needs and preferences? How can engineers ensure their technical solutions are functional and resonate with customers? How does someone become a data scientist after college, and how do they progress in their career? How do you gather and use customer feedback to improve your data models and technical features? What advice would you give engineering project managers to make their data science projects more customer-centric? Here Are Some Key Points Discussed in This Episode About Innovative Collaboration Strategies for Data Science and Engineering Teams: Collaboration between data science and engineering teams ensures that customer perspectives are integrated into data projects, making technical solutions more effective and impactful. This teamwork helps address challenges like stakeholder support and necessary infrastructure. Engineering practices, such as starting with a design document and maintaining clear communication, ensure data solutions meet customer needs and preferences. These practices help align projects with customer goals and prevent unnecessary or misguided efforts. Engineers can ensure their technical solutions are both functional and resonate with customers by involving them in every decision point of the project. This continuous engagement helps align the solutions with customer needs and preferences, ensuring effective and relevant outcomes. After college, one can become a data scientist by starting as a data analyst, working with databases and reports. One can then progress through roles like senior data analyst or data scientist by gaining experience and enhancing their technical skills. To improve data models and technical features using customer feedback, listen carefully and rephrase your input to ensure understanding. Focus on understanding their goals before diving into technical solutions, ensuring alignment with customer needs. To make data science projects more customer-centric, engineering project managers should create cross-functional teams comprising engineers, project managers, stakeholders, and potentially data scientists. This enables better collaboration and understanding of customer needs, resulting in more effective outcomes. More Details in This Episode… About Akshay Swaminathan Akshay Swaminathan is a data scientist who works on strengthening health systems. He has more than 40 peer-reviewed publications, and his work has been featured in the New York Times and STAT. Previously at Flatiron Health, he currently leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine. About the Host: Matthew Douglas Matthew currently serves as a content creator and host of The Engineering Project Management Podcast. A civil engineer by trade, Matthew has developed a passion for construction and stormwater management by way of maintenance and rehabilitation services. Matthew has also had experience working under private consulting firms and public agencies, and has even held a role as an educator. As such, he loves to lead, build, mentor,