Head of Data Analytics and Data Science at HyreCar Inc.
Linda Liu has extensive experience helping a business build analytics and data science foundations as well as roadmaps that empower data-driven decisions and improve user experience across the organization. She is also a big advocate for cultivating a data-centric culture that enables teams across the business to better utilize data/analysis and bring added value to the day-to-day work. After receiving her MS degrees in Statistics and Economics from the University of California, Santa Barbara, she has worked in data analytics and data science for a number of businesses in the past ten years, including technology, SAAS, e-commerce, communications, mobility, etc. In addition, she has worked for many different departments, including Marketing, Sales, Product Operations, Customer Support, Growth. This unique set of experiences gives her a better understanding/insight into the value data analytics can add to different stages of the customer lifecycle. In her current role at HyreCar, Inc. leading the data analytics and data science team, she works closely with executives and business stakeholders to continuously drive insights to guide business decisions and improve user experience using advanced data analysis and machine learning models. This has fueled the business growth by ensuring high-quality metrics tracking, identifying opportunities for growth, and reducing operational expenses.
WATCH LIVE: December 1 @ 4:20PM – 4:50PM ET
How can we systematically enhance the prospects of conducting conclusive A/B Testing to benefit the business? Everyone is familiar with the concept behind A/B Testing as it is simple and intuitive. Perhaps that is one big reason why A/B Testing is so wide used. It is one of the most important tools in data science and in the tech world in general as it is one of the most effective methods to draw conclusions about any hypothesis one may have. In some ways, it is a victim of its own popularity. It turns out not too many companies can carry out A/B Testing program successfully. Sure, everyone has a few winning A/B tests that he/she can be proud of and present at conferences. However, only select businesses have achieved real, long-term success via continuous and strategic experimentation. Why is that the case? The devil is always in the details. A holistic approach is needed to ensure external and internal factors are accounted for. A data scientist can play a key role in that respect. A data scientist’s role goes beyond merely measuring the results. I want to share how engaging a data scientist from inception to completion will systematically enhance A/B Testing. In other words, putting together an A/B Testing framework: from promoting an A/B Testing culture, to collaborating with various functions for idea generation, to identifying metrics to measure performance, to implementing the right technical approach, to monitoring, result intake, and possible follow-on operationalization, etc. I will also share examples on possible pitfalls and suggestions on how to avoid them so we can conduct conclusive AB Testing to benefit the business.