Thirty-three. That’s the percentage (33%) of Data Science job vacancies that get filled across the Asia-Pacific region (Australia included). There is clearly a strong demand for data scientists, and not enough talent to go around. And the demand for it keeps getting bigger.
Since 2012, there has been an increasing demand for data scientists around the world, as you can see from this chart on job postings:
Why the strong demand for data science?
Imagine if you were a health insurance company. Would you be interested in (with the use of smartwatches) monitoring your customers’ heart rates, sleep patterns and physical activity to incentivise good habits? Or if you were a commercial airline, wouldn’t you want to track your pilots’ flight patterns and see how to improve efficiency? These could be worth millions of dollars in additional profits, and are exactly what MLC and Qantas want to accomplish with data science.
Big retail industry players such as Woolworths, Wesfarmers, Myer, JB Hi-fi and the Super Retail Group have already made huge investments into data science, such as the purchase of Quantium (for $20M five years ago) by Woolies. Mining giant Rio Tinto is also moving aggressively into data science, expecting to double their data scientist hiring in the next ten years.
Do you need data scientists?
Before diving headfirst into the data scientist pool, here are a few tips you’ll need to check first:
- You’ll need historical data. Without data to work with, your data scientist will be like a sports car with no fuel. Your company should not just have a number of data sources in place, but it should also have a high volume of historical data as well.
- Data science works best in fast-moving situations. Building predictive models on high-volume industries is much easier than in those where business transactions take months, or years, to conclude (i.e. B2B companies). With the passing of time, more and more variables can come into play that may affect a business outcome. In other words, the longer a studied behavior will take to conclude, the more difficult it is to predict it.
The talent pool for data scientists in Australia
Given that there is an ever-growing demand for data scientists, the challenge now lies in the supply of it. As we said earlier, only 33% of data scientist jobs are ever filled in Asia Pacific. Aside from expediting homegrown talent, offshore hiring seems to be the best option to stem the shortage.
Rio Tinto CEO Jean-Sébastien Jacques said in the CEDA’s Copland Lecture in Melbourne last March, that for the company to fulfill their need for data scientists, they will have to start looking abroad for talent. “The challenge and opportunity [are] either we find a way to train people in Australia or to bring those people to Australia,“ he said.
Off-shore recruiting firms such as Kinetic Innovative Staffing have already started sourcing data scientists for Australian businesses. Kinetic’s model can provide data scientists working remotely from the Philippines to Australian businesses. This avoids the various difficulties faced with relocating foreign talent, as these scientists can work almost immediately from the confines of their homes abroad.
An additional benefit that comes from off-shore remote workers is that hiring rates can be significantly lower as compared to domestic rates. Due to significantly lower costs-of-living in countries such as the Philippines, offshore talent can require salaries that are up to 75% lower than local hires.
Jacques adds, “The reality is if we cannot bring [data scientists into Australia] fast enough then I’ve got no other options but to find them elsewhere. It’s why lots of conversations are taking place at the federal and state level with universities and the industry to see how we can unlock the situation. It’s not easy.”
Jacques said there is a need for a “multi-year roadmap on how we train the people for the next 5-10-15 years.”
If you would like to know more about building up your data science team, get in touch with us at Kinetic through here.
