What specializations are key to building an effective data team? with Nikhil Goyal
The Data Standard
Episode Summary
In this episode, guest Nikhil Goyal sits down with TDS to discuss which specializations are key to building an effective data team. Prior to LeafLink, Nikhil led the data science function for Gartner’s Marketing practice, gaining deep expertise in building scaled data products for large consumer brands. He has extensive experience in generating insights from e-commerce, search and transactional data leveraging experimentation, machine learning, and implementing business intelligence solutions. Nikhil has also held positions managing data science teams at and consulting F500 clients on marketing and advertising. Currently, Nikhil is responsible for continuing to evolve the data platform that powers the LeafLink marketplace.
Meet The Host
Shayna Weldon
Podcast Host at The Data Standard
I aspire to broadcast content that carries my curiosity for the world and captivates an audience. I was driven to engineering by my passion for problem-solving, but I realized that while I enjoy the challenge of analytical work, I belong in an industry focused on relationships. I want to connect industry leaders, teachers, and athletes that make our world so unique and present their insight to the public. I have a natural inclination to reach out to people and hear their stories and I will exploit this desire for the rest of my life.
Meet The Guest
Nikhil Goyal
VP, Data Engineering & Analytics at LeafLink
I love bringing the power of data science to any decision-making, especially in the marketing domain. I am a practitioner who loves experimenting, learning & doing new things. For the last couple of years, I have hired and managed a team of data scientists that sits at the intersection of engineering, product, and business analysts. We build data science pipelines, prototype data products, and analyze all the data sources of the company to find insights that power the research & client recommendations the company publishes.
