Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling.
In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality.
Some problems Greg started looking into while working at Telstra were fixed by using the four P's of marketing. He had an opportunity to learn something new that they did not teach in engineering school. In business, the four P’s are a useful lens to think about commercial problems like product lifecycle management and portfolio optimization for business. They looked at questions around what products will work well in what channels. Previously, this type of merchandising decision making was done by gut feel. Having a data-driven approach was a different way of thinking for the company and the teams. Greg would not recommend someone to gain a Ph.D. to become a data scientist. You can acquire the skills you need outside of academia. Academia will not give you the skills to become successful, a Ph.D. may hinder requiring all the skills to become a data scientist. Later, Greg discusses his appreciation for managing data scientists, being involved in the local data community, and the challenges of working globally.
Enjoy the show!
We speak about:
[02:00] How Greg started in the data space
[11:10] Leaving academics and getting involved in the industry
[13:20] Greg’s work background
[18:25] The four P’s of marketing
[20:40] Transiting from gut instinct to a data-driven approach
[27:55] Thinking through cause and effect
[30:45] What Greg’s team looks like
[39:00] Lessons learned from managing data scientists
[42:25] Active in local data science meetups + guest speaking
[44:25] Working globally + peeling back opportunities to use data science techniques
Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au
“My thesis was not a project; it was a lifestyle.”
“I didn’t want to be an academic, I wanted to get back into the industry.”
“It was a combination of arrogance and laziness.”
“At the end of the day, it boils down to if I change X, will Y change?”
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Gregory Hill is based in Melbourne, Australia.
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