Koo Ping Shung, Co-Founder/Practicum Director, Data Science Rex Pte. Ltd.

Koo Ping Shung has almost 20 years of experience in Data Science and Artificial Intelligence. His experience spans a wide variety of industries and the whole data value chain. He is an instructor, facilitator for various institutions, both locally and overseas. Koo is an advisor to businesses and startups and has mentored many individuals. He is a speaker and panellist at various conferences too. His strong passion is shown by co-founding DataScience SG, one of the largest tech communities in the region. He was previously the President of the AI Professionals Association, heavily involved in the professional accreditation process for AI Engineers.

Recently, in an exclusive interview with Digital First Magazine, Koo shared his insights on how data science can empower businesses, the inspiration behind establishing Data Science Rex, his professional journey, success mantra, pearls of wisdom, future plans, and much more. The following excerpts are taken from the interview.

In today’s rapidly evolving business environment, data science is considered as a catalyst for business growth and success. In what ways does data science empower businesses?

Data Science empowers businesses in many areas but all in all, is to make better decisions. In business and in personal lives, our current circumstances are a result of all our past decisions. As such, making good decisions, decisions that can improve business and personal lives, is extremely important.

To make good decisions, a key ingredient is information and that is where data science comes into the picture. Data science can help us to comb through the data using different mathematical algorithms or just simple rules to gain insights that will be valuable, that provide us with a clearer indication of which decision is good to take up or bad decisions to avoid.

And it is pretty natural. Think about the last time you chose a restaurant for your lunch in a new city. What did you do? Do you search for restaurants with good reviews? Where does the review come from? They are data provided by individuals who were in the same situation.

Data Science empowers businesses and individuals to make better decisions, and in turn, move towards a longer-lasting business or a more comfortable life for individuals.

What was the inspiration behind establishing Data Science Rex? What sets it apart from other market competitors?

I strongly believe that a lot of companies will benefit from data, as technology improves and decreases the cost of data collection. When I run tech communities and in my current role, I see many companies struggling to make sense of the data collected.

I strongly believe that going forward, data professions are here to stay. I hope to inspire more passionate talents to join the data industry. Putting these together, I want to help more companies make sense of data through hiring good talents to take advantage of data.

This is where Data Science Rex comes in. We are a group of data professionals who are very passionate and keen to help companies build up Data and Artificial Intelligence capabilities. Unlike other consultancies who want to work on projects, Data Science Rex is keen to do knowledge transfer to our clients through training, consulting, and mentoring to help companies build up the necessary capabilities to bring their data to the next level. Our success comes from clients being able to build up more data use cases and hire more data talents.

Koo, please tell us about your past work experiences, career growth journey, and your long-term vision.

My major is in Economics with a minor in Mathematics and Computational Finance. I graduated during the SARS crisis, and it was very brutal for fresh graduates back then, but I held on doing part-time jobs. After about 18 months, my friend made a job recommendation, and I started my first job as a research assistant in an Education and Pedagogy Research Center. During my research stint, I took the opportunity to be a software trainer as well, because I wanted to increase my skill set, especially communication skills. Because of my skill in the software in which I train people, I managed to get into the banking industry as a credit risk analyst. I manage the data server for my team and work on a few analysis projects picking up knowledge and skills in data management. One of the memorable projects I did was to use credit bureau data to identify customer segments that have good risk-reward ratios. I was on the credit risk management team in two banks for about 4 years, completing my MBA at the same time.

Back then, I felt that banks did not really take much advantage of machine-learning algorithms and wanted to learn more about their applications. This is where the next opportunity came. I was asked to join a local Analytics master’s Programme as a Practicum Manager. What a Practicum Manager does is liaise and consult businesses and scope data science projects for master’s students. Basically is to manage the industry relationship for the Master’s Programme. I also got to supervise master’s students on their data science projects. It is about that time I started DataScience SG, currently one of the largest Data Science tech communities in Singapore.

As the years progressed, I felt that I needed change but had no idea which industry to go into, so I resigned and wanted to take a career break. When news of my break came out, I got requests asking me to do training, consulting, and mentoring. One thing led to the next, I now run Data Science Rex where I conduct training at various institutions, consult a few startups and accelerators, and mentor a few passionate talents.

My long-term vision is to create an ecosystem, spanning across regions, that allows companies to flourish with their data science and AI capabilities, and professionals doing data science and AI can be gainfully employed. I hope to get more passionate individuals onboard, to consider data and AI as their long-term career. And at the same time, help more businesses be able to tap into AI, to use AI more effectively, efficiently, and more importantly, ethically.

What are your thoughts about ChatGPT? Do you feel that data science jobs may become obsolete or can the AI chatbot help data scientists in diligent tasks?

I like Generative AI like ChatGPT, StabledDiffusion, etc. Text Generative AI like ChatGPT is one of the best research tools I can ask for. I mean, where do you find a research assistant that has read that extensively in the first place? Granted, it does “hallucinate” and provide the wrong information, but it does not mean humans do not. This is where critical thinking comes into the picture. Generative AI like ChatGPT, I felt actually enhanced my productivity, in terms of writing, researching for ideas, translations, learning established topics like ancient history and philosophy, etc.

Moreover, I cannot draw well enough to save my life, this is where Image Generative AI comes into the picture where I can get decent drawings using text prompts for my work.

I am rather a strong believer in Augmented Intelligence. Knowing how to question the data to solve problems is where data scientists are generating the most value for their employers. Generative AI will help data scientists to be more productive, focusing more effort on asking the right question on data to solve business challenges rather. The most valuable data scientist will be the one that is able to use the tools to enhance his/her capabilities. Thus, I am not worried Knowledge Economy jobs like Data Scientist will be obsolete unless by choice.

What key personality traits are common among successful data scientists?

There are a few actually. Firstly, they are passionate about the field. They get excited looking through data and crunching those data for insights. Secondly, they are always open-minded and willing to learn and pick up new things, which is very important. Another trait is they like numbers and are sensitive to numbers i.e., they know how numbers and data work and thus understand how to “question” the data.

Last but not least, they have very good presentation and communication skills. The data science job requires communication across different teams and being able to speak different “languages” helps in that aspect. The successful data scientist knows how to communicate well, get buy-in from other relevant stakeholders and move in the direction together in solving the business challenge.

Do you think non engineers can perform well in analytics?

Definitely! I feel that people who are sensitive to numbers, and who are able to ask data the right questions are going to succeed in analytics and data science. It really does not matter if you come from a computer science background or social science background, as long as you know how to crunch data, and know the right questions to ask the data, you can perform well in analytics for sure.

What, personally, has allowed you the success you have had in the role of a leader in technology?

I personally feel my role as a leader in technology is to allow other people to shine, and to give them the necessary guidance and resources for them to succeed. The more people who succeed, the better I feel as a leader, that I have helped others to grow. This mentality also motivated me to learn more, to synthesize information and knowledge more. The more I learn, the more I can share to help others to grow, to understand the profession and industry better.

What is the best career advice you’ve received and how have you sought to put this into practice?

There are a few lessons I have experienced in my career. Firstly, do not fall for impostor syndrome. If an opportunity arises and you feel that you are 80% ready for it, do not say “No” to the opportunity! Take it up and wing the 20% along, learn fast even from mistakes! Because a fantastic opportunity is rare than an opportunity you are able to pick it up and succeed with 100% confidence! I would not be where I am if I waited for myself to be 100% confident to take up the opportunities that came my way.

Another lesson I have experienced is results speak for themselves. Constantly think about how you can create a win-win situation for the other party and yourself. There will be people who will hear about the positive results you have through word of mouth and reach out to you. Good opportunities come knocking this way mostly or at least that is how I get my opportunities. Take up opportunities, work to get the best results you can, and move to the next one.

Last but not least, stay curious and keep learning! If you find someone smarter than you, invite the person into your life as a mentor. Getting a mentor really helps a lot in your career and life.

Which technology are you investing in now to prepare for the future?

I am always curious and always looking to help those around me to be better together, so any technology that helps me to achieve that is what I will invest my effort and time in learning.

Learning how to learn effectively and efficiently will be a fantastic skill to have since AI will slowly but surely replace more and more tasks, while a human will move on to higher value add tasks that require more thinking, such as critical thinking, design thinking, strategic thinking, logic thinking, etc.

What advice would you offer others looking to build their careers as data scientists?

Ask yourself if you are passionate about data. If you are, congratulations you have taken your first step. The next step is to build up your own personal data project portfolio. Do not use any school project because that is what others might do and it will be difficult to differentiate yourself out. Rather, work on data projects you are passionate about, and document them in code repositories, blogs, etc. Then share them with prospective employers.

Why a personal project portfolio rather than certification or degrees? Because your employers are keener on people who have demonstrated the ability to turn data into gold rather than papers that say you have the ability.

Once you get into being a data scientist, start to learn how to learn because being in the data science and artificial intelligence field, there are new tools and knowledge being developed and thus it is important for us to keep learning, to keep the momentum on helping our employers to solve problems with data.

Why did you co-found DataScience SG tech community and how did you grow it to a few thousand strong on Facebook?

DataScience SG was co-founded almost a decade ago in late 2013. Back then there were too many people trying to steer conversations to fit their personal agenda, like selling their own training classes or consulting services. My co-founder and I felt that for the industry to grow healthily, we need to provide a platform for people to learn what Data Science (back then) and (now) Artificial Intelligence really is about. That is why we set up DataScience SG. We hope to get more and more people to really understand what Data Science and Artificial Intelligence are about and whether it is worth building a career in it. We wanted DataScience SG to be a platform for people to learn, network, and attract job opportunities.

I will say that we grow it to such a large community because we are always focusing on content and do not allow too many ads to be shown to the community. We try our best to ensure we get the best speakers to come and share their knowledge and not sell too much. We are very glad that it is working out and that DataScience SG is still relevant, as proven by the huge attendance we attract even after the Covid pandemic.

What else do you do to advocate for Data Science and Artificial Intelligence?

I wanted to gain experience in a lot of other mediums as well, so I do have a website where I write my thoughts on the latest in Artificial Intelligence or my research in Artificial General Intelligence. I just re-launched my newsletter as well for followers who want my content straight into their inbox rather than filtered through social media algorithms. I also did podcasting where I shared my thoughts and I invited guests to share their data career journey, as references for anyone keen to kickstart their data career to learn from.

What kind of topics in AI excites you tremendously?

One of the people I follow closely is Demis Hassabis from Google Deepmind. He set up Deepmind wanting to “Solve Intelligence”. This is a topic that I am very excited about. I am very keen to understand more about Intelligence, intelligence in humans and machines. And when I read more about Intelligence, it leads to other topics that I am very interested in like Philosophy, Psychology, Cognitive Neuroscience, and Mathematics. Yes, “What is Intelligence?” will be the topic that will excite me the most!

Content Disclaimer

Related Articles