Michael Wang: Creating Data-Driven Solutions for Investment Industry

In an era defined by data-driven decision-making and rapidly evolving financial markets, firms across the investment industry are seeking innovative solutions to harness the power of data science and artificial intelligence(AI). WhyPred is a dynamic and forward-thinking consultancy combining data science and AI expertise with a deep understanding of traditional financial markets, trading, and cryptocurrency. We offer highly curated structured and unstructured datasets, research and analytics services, and consulting on incorporating generative AI into existing investment processes.

A Journey from Financial Market to Data Science and AI

Michael spent the first part of his career in financial markets working in analytics, research, and portfolio management roles across multi-asset, equities, and trading. Through those roles, he gained an appreciaton for  the depth and breadth of data involved in decision-making within the investment industry. He also realized there had to be a better way to distill actionable insights and separate the signal from the noise. This was when Michael became interested in AI and data science. In one of his roles as a young analyst, he was fortunate enough to talk to some of the biggest fund managers in the world about how they used machine learning and AI in their processes. Michael decided to return to university to formalize his data education, after which he became a data scientist.

His data science journey began at a hedge fund where Michael helped to formulate and test machine learning strategies; he then worked on an analytics project for one of Australia's Big Four banks, followed by a stint teaching AI and fintech at the University of Sydney. He is currently the Head of Data Science at CreditorWatch, Australia's largest commercial credit reporting bureau, and the Principal Data Scientist at WhyPred. At WhyPred, he focuses on creating actionable intelligence and deep research insights using traditional machine learning methods and generative AI.

Understanding the Business

Michael says, "One key lesson is that data science is just a set of tools that are only useful with a domain; therefore, to solve any problem with data science, you must start with understanding the business problem and the domain within which the business operates. This is ingrained within me as I started my career in investments." There are domain-specific nuances in working with financial data that need to be well understood before applying machine learning to problems of that nature. He stresses always understanding the data and the domain before considering which algorithms should form part of the solution.

Passion and Persistence

Michaels asserts, "Making the career switch from investments to data science took much work. It involved teaching myself to code, attending countless meetups, returning to university for 3 years, and lots of sleepless nights spent reading and working on projects over 7 years. What ultimately got me through was the passion I had developed for data science along the way and the desire to apply my newfound skill set to real-world problems. With hindsight, it was time well spent with no regrets."

Sharing Knowledge

Michael gives out some of the vital attributes that every data science leader should possess:

First, the ability to articulate complex concepts in a simple and easy-to-understand manner. This is often an overlooked attribute, but it is the role of the data science leader to communicate technical developments to key stakeholders, and often those key stakeholders need to come from a technical background.

Second, retaining a holistic view while solving business problems with data science. The end goal isn't to optimize an objective function or reduce the error rate; the end goal is a real-world outcome like improving the customer experience or reducing drawdown risk in a portfolio.

Third, resilience this an attribute not just for data science leaders but all leaders, the ability to persevere under pressure and inspire your team in difficult situations.

Understanding Clients and Incorporating New Technologies

Coming from an investment background already gives a good starting point as Michael has been in the shoes of his client/target audience and understands their needs and pain points; in addition, he constantly keeps an eye on top of recent developments in the industry and continuing to iterate their product and services to incorporate new technologies such as generative AI.

Embracing the Change

Michael explains, "We're on the precipice of a new technological revolution with AI. As practitioners, we must embrace this change safely, responsibly, and ethically. In addition, as data science leaders, we need to maintain an open mind and continue to look for ways to innovate and improve on existing paradigms of data science."

A Bright Future Ahead

Michael says, "The investment industry has always been a heavy data consumer; this won't change anytime soon. The rate at which financial data is created and captured will continue to grow at an accelerating rate, especially with the emergence of technologies like AI and cloud computing." Therefore, he foresees the opportunity set for WhyPred's products and services to grow in kind, as with more data comes the need for more insight and intelligence.

Sharing Advice for the Future Leaders          

Michael has three key pieces of advice for the emerging leaders

• Stay humble in the pursuit of knowledge,

• Don't be afraid to fail as to succeed, you need first to know failure, and

• Don't burn bridges because no matter how big the world seems, it will come back to bite you.