Technologists, hone your competitive edge through investment insights

How can data and technology professionals keep themselves in hot demand as the global economy cools?

Become professionals who have not only quantitative techniques and tech skills, but also market awareness, say two quantitative researchers at GIC.

“There is clear market demand for technologists who are business-focused, agile and results-driven,” says Jingyuan, a data scientist under GIC’s Investment Insights Group (IIG).

“The market recognises the need to hire technologists from diverse disciplines and backgrounds,” she adds. “It is becoming more aware that junior investment analysts should have the ability to code and derive investment insights from large scale data. Job postings for analysts now list such skill sets as a key hiring criterion.”

Adds IIG’s Head of Public Equities & External Managers Department Quantitative Research, Xin Zhi: “The exponential growth of data, as well as advancements in computational power, created a race to process more data, more effectively, and more quickly – and roles have pivoted accordingly towards this.”

“So, while demand for people with deep knowledge in a single domain will not go away, there is growing demand for people who are at the intersection of the three domains – quant, tech, and markets.”

It is precisely talent with expertise across all these domains who will soar at GIC, which is looking to hire technologists across a spectrum of experience levels – from fresh graduates to mid-career professionals – to use advanced quantitative techniques and tech tools to generate data-driven insights that fuel new investment ideas and enhance performance.

Candidates with academic training in engineering or the hard sciences are particularly desirable, as are those with experience in investment management making mid-career switches.

New joiners can expect to work with GIC’s investment teams to augment processes by delivering data-driven analysis and insights. “GIC invests globally across multiple asset classes, which brings about plenty of opportunities for interesting research projects,” says Xin Zhi.

She adds: “Previously, technologists were only expected to ‘keep the lights on,’ but now we are looking for people who will push the boundaries in terms of investing impact, which includes originating and developing ideas for investment research.”

In return, GIC begins grooming talent right from the junior level, designing career paths for everyone to fulfil their potential through mentorship, ongoing training, and opportunities to move across asset classes and functional domains.

“With GIC’s size and global coverage, we offer our people the platform to build their careers on,” Xin Zhi says.

Building on a technical skillset

For Jingyuan, joining GIC has given her the crucial opportunity to solve business questions that call for data-driven answers.

“In my previous role, I used to get very well-defined requirements from users for features that need to be developed,” says the data scientist, who spent five years as a software engineer at an investment bank.

“But in my current role, I conduct independent research and analysis, work with stakeholders to define a systematic framework, and source for datasets that eventually feed into GIC’s machine learning algorithms.”

Jingyuan joined GIC in 2021, seeking to further develop her interest in machine learning, and seized the opportunity to play a critical role in the organisation’s ambitious roadmap for Chinese Natural Language Processing (NLP).

At first, the data scientist studied the applications of artificial intelligence for the organisation, and the potential uses for advanced NLP methodologies in projects.

But further communication with business teams revealed that this direction was “not very meaningful.”

“Oftentimes, simple problems can be solved using a non-NLP approach,” says Jingyuan. “So, we pivoted to understanding business thought processes on a deeper level and tailored our approach to producing bespoke solutions that address incredibly specific investment questions.”

Today, she focuses on the organisation’s China remit, working with equities teams to provide investment insights using NLP methodologies.

Her takeaway? Frequent engagement with business teams helps quantitative researchers gain deeper context around business challenges. As a result, they can design more useful and more widely adopted analytical tools, instead of “plainly serving the developed models in their raw form”.

Adds team head Xin Zhi: “Having a technical skillset is not enough – we need to be able to apply it meaningfully. This is something you can only glean from practitioners, so being part of the business is key.”

This, in fact, is part of what makes working at the organisation appealing for Jingyuan.

“GIC allows us to take full ownership of the project,” she says. “Besides working on machine learning models, we brainstorm project ideas, develop tools that find trends and themes systematically, and translate insights in a manner that a portfolio manager can understand. Such a unique experience helps us see how our work can make a business impact.”

Work at the Point of Impact. Learn more at GIC.CAREERS