Jarrod Teo is the Chief Data Scientist for Direct Sourcing Solutions, a company that provides services that help businesses improve their overall requirements and strategy, leading to a refined and enhanced transformation, management, and reduction of costs.
- His career in Data Science, the trials and tribulations
- Advice for potential and current Data Scientists
- Recommendations on skills and Books
“I look first at the business objectives. When we start a data science project at Direct Sourcing Solutions, we use this sequence: Business objectives > Data we have > Data science methods to use > Software to consider.”
Nisha: At what point did you realise that you wanted to pursue a career in data science (data & AI), and how did you get into it?
Jarrod: When I was in junior college I got a perfect score for statistics. That was when I first thought of using numbers to help solve problems. There was no data science back then, so I enrolled in a statistics degree course at the National University of Singapore.
Nisha: What unusual or absurd thing do you practice or advocate for in your profession as a data science leader?
Jarrod: This might seem unusual to some data scientists, but I don’t look first at what software we should use. I look first at the business objectives. When we start a data science project at Direct Sourcing Solutions, we use this sequence: Business objectives > Data we have > Data science methods to use > Software to consider.
Nisha: What advice would you give to other business leaders who would like to step into realising data science use cases? What advice should they ignore?
Jarrod: Plan for the cloud but don’t use it until you are very convinced that the machine learning model is earning money. Start the machine learning model offline with a lower budget first because there will be experiments and trials with the data on hand to show you can solve the business objective. If you start with a cloud environment, costs can build up and undermine support for the project. So, the project must make money first offline and with a lower budget.
Ignore anyone who tells you machine learning models can be produced fast in any situation. I had a senior manager once who thought a new machine learning model could be created within seconds and without looking at the data. Machine learning models take time to build. Also, not all business objectives need machine learning models.
Nisha: What skills and attitudes do you look for when hiring data scientists?
Jarrod: As the Chief Data Scientist of DSS, I look for data scientists who have a statistics background and are creative. Yes, coding is important, but they can pick up software along the way. To be creative and able to read the output from the statistics software is very important.
“With AI now being used consistently, the topic of the moment is: What if our AI-trained models are hacked?”
Nisha: In your opinion, what have been the most relevant breakthroughs in data science impacting our world in the last 1-2 years, and what trends do you see emerging going forward?
Jarrod: AI Security. With AI now being used consistently, the topic of the moment is: What if our AI-trained models are hacked? Leveraging AI to identify cybersecurity attacks is a trend that can get more focus going forward.
Think about retail, for example. With the increasing prevalence of customised recommendations of products to customers, an attack on the AI pipeline can allow customised information to be obtained. Expect more interest in understanding how machine learning models can help to uncover patterns in cybersecurity attacks.
Nisha: What is one book that you would recommend young data scientists to read?
Jarrod: Data Mining Applications for Small and Medium Enterprises by Prof. Koh Hian Chye
This book shows practical use cases which can help young data scientists understand how machine learning models were used in actual projects. Prof Koh is also an experienced and well-known data scientist. I worked alongside him on projects when I was in working at IBM SPSS more than 10 years ago.
Nisha: What lessons have you learned on getting the company’s bought into leading through data versus gut?
Jarrod: Do not explain things using jargon. This is where the acceptance of outcomes from data will fail. Business people do not want their one-hour presentation on making money to become a statistics lecture. They only want to know how to make money.
Nisha: How did you get into the tech field?
Jarrod: I started as a data analyst to learn the foundations. After that, I did data science projects with real data, some of which were exceptionally difficult. For example, creating a machine learning model without data, proper hardware, software, and server. I had to piece all these together and create a data science environment to work.
Nisha: What and where did you study?
Jarrod: I was a statistics major and a math minor at the National University of Singapore.