Whether you’re figuring out your first career path or looking to change directions, a new series from Global News, Hot Jobs, focuses on career strategy for a new era in work.
Imagine landing a $100,000 job straight out of school and without the oppressive student debt that usually comes with an MBA, a law degree or at the end of medical school.
Until recently, that was the stuff of Silicon Valley dreams. But a Global News inquiry suggests that top graduates in Canada’s own tech industry are increasingly crossing the six-figure mark for entry-level jobs that don’t always require an advanced degree.
“There’s a one per cent unemployment rate in IT in Canada right now,” said Jonathan Ward of Ward Technology Talent, a Toronto-based recruiter.
The IT world Ward is talking about has little to do with the traditional tech-help desk in charge of keeping company computers running and dealing with the inevitable printer jam. Even including more traditional IT jobs, the unemployment rate was a low 2.6 per cent in the sector in 2017, according to Canada’s Information and Communications Technology Council (ICTC).
Instead, what’s driving up salaries for tech graduates is cutthroat competition to hire the best and brightest tech graduates to work on data analytics, artificial intelligence (AI), and, more recently, blockchain.
It isn’t just startups racing to snatch Canada’s top tech talent. Corporate giants from virtually every corner of the economy — be it big banks, insurance companies, law firms or retailers — have joined the fray. Some have built entirely new work spaces — like Scotiabank’s Digital Factory and Loblaw Digital with loft-like ceilings, exposed brick, and things like ping-pong tables and bowling alleys — that reproduce what corporate wisdom holds as the natural environment for young techies.
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The volume of AI-related job postings on online employment search site Indeed Canada has more than doubled over the past year and a half, according to data provided by the company to Global News.
Before 2017, such openings were rare, said Brendon Bernard, an economist at Indeed. Last year, he added, is “when the action really started.”
The boom in AI-related jobs has come as faster and cheaper computers allow companies to sift through enormous volumes of data to spot useful patterns and make predictions. And coders are using big data to train so-called artificially intelligent machines, software that is capable of autonomous learning. The applications are seemingly endless: from the ability to recognize and tackle fraud in real time, through predicting customers’ needs to teaching a car to drive itself.
Blockchain, meanwhile, makes it possible to create permanent digital records of transactions, a technological feat that has piqued the interest of the financial industry, among others.
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All this has created formidable demand for a limited number of graduates who can create a piece of software that can learn to sift through legal documents or predict a spike in hospital bed occupancy.
It’s hard to know exactly how many such graduates Canada produces every year, because they may come from several different departments and universities generally track numbers by faculty. But one thing seems clear: employers are willing to pay big bucks to attract them.
According to Indeed, the average salary for data scientists is around $104,000.
A smaller data set provided to Global News by Advanced-HR, a San Francisco-based source of compensation data from venture capital and private companies, shows that salaries for entry-level data scientists in Canada are typically between the mid-$50,000 and mid-$70,000 but can reach $100,000.
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The data is similar to what you’d see for larger companies traded on the stock exchange, said Dee DiPietro, founder and CEO of Advanced-HR.
The highest salary in that data set was $104,000, which was for a Canada-based data science job at a U.S. company, “where the Canadian pay practice has been influenced by a different practice.”
But that could become more common as the U.S.’s politics and tough stance on immigration make it more difficult for Silicon Valley to attract Canada’s top tech brains.
“Trump is starting to work to our advantage,” Ward said. “It’s getting harder to find people to cross the border.”
And anecdotal evidence suggests that the $100,000 entry-level job is no longer a unicorn for Canada’s data scientists.
Star grads can hope for base pay of around $100,000 “in many cases,” said Larry Smith, director of the Problem Lab at the University of Waterloo and a longtime informal career counsellor at the school.
While $100,000 is “on the high-end” of what an entry-level data scientist can expect to earn, it’s possible for someone to reach the six-figure mark straight out of school, said Colin Fraser, a data scientist at CHIMP, a Vancouver-based online platform for charitable giving.
The salary floor for such jobs, he added, “is no less than $80,000 [a year] — and that’s for junior-level.”
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There is no degree in data science or AI. Usually, graduates taking up those jobs have majors in mathematics, statistics, computer science, or computer engineering, Smith said.
Jobs in data analytics are accessible with a simple bachelor’s degree, while those in AI usually require an advanced degree, he added.
Employers usually set up shop on campus in September to try to spot and attract top-notch techies at career fairs. In such a tight labour market, recruiting is a two-way street: It’s as much about finding the right people as it is about convincing them to come work for you instead of a competitor.
Still, the screening process is tough.
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For example, landing an entry-level job at Swift Medical, a Toronto-based medical technology startup, typically involves three interviews. It starts with a video call where candidates are asked about their interest in working for Swift and its mission of helping to improve hospital care. Then there’s a one-to-two hour in-person interview that includes lots of scribbling on a white board, as well as non-technical questions.
And, the tech industry, like investments banks and global consultancies, is fond of brain teasers. Google, for example, reportedly used to ask questions like: How many golf balls can you fit into a school bus? And, how many piano tuners are there in the entire world?
Teasers are one of the first things we do,” said Laura Niblett, who handles HR at Swift. They allow recruiters to see how candidates think on their feet and how they react to a question to which they don’t know the answer. The point, she added, to to gauge people’s attitude, more than their technical abilities.
Your frame of mind and disposition matters, Ward said. In an field that’s constantly evolving, companies want to ensure that you are both eager to and able to learn on the fly.
Another must-have, he added, is co-op experience.
An engineering degree usually attracts attention, Niblett said. But someone with a degree in mathematics and lots of co-op experience in data analytics looks better on paper than an engineer with no internships on her or his resume, she added.
In general it pays off to customize your university education, Smith said, be it via co-ops, side projects like starting your own business, and learning a few extra skills on your own.
Still, he added, the idea that you might be able to skip university is mostly a fantasy.
“In almost every case, [employers] will hire someone with a degree.”
It’s about adding to your university education, he said, not replacing it.
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The job market for AI-related jobs is so young that there isn’t yet a broad consensus on what popular job titles really entail, Fraser said. Employers themselves may not always fully understand what specific role they need to hire, he added.
Generally speaking, however, four common job titles are: data analyst, data scientist, data engineer and machine learning engineer.
Data analyst. The job entails slicing and dicing data to look for relevant trends and what may be behind them. For example, a data analyst working at a telecoms company may look at company data to spot changes in customer churn rate and come up with hypotheses about what might have cause an unusual spike or decline in those numbers, Fraser said. These kinds of jobs have been around for some time but have recently started to include the use of some more sophisticated analytical techniques tied to A.I, he added.
Most data analyst have a university education in a quantitative field, but, with a bit of “hustling” it is possible to get the gig without a degree, according to Fraser.
The average salary for a data analyst is just over $61,000 according to Indeed. But Fraser said the job title has come to include such a broad spread of responsibilities that pay could be as low as the mid $40,000 for “someone looking at spreadsheets” and as high as the low end of what a junior data scientist would make.
Data scientist. While a data analysts focuses on analysts existing sets of data to figure out what happened, data scientists use the data to come up with models to forecast what might happen next, Fraser said. At a more advanced level, that involves designing algorithms that will ideally learn to make better and better predictions. Data scientists often have advanced degrees and even PhDs, said Fraser, who confessed he felt “under-educated” with his degree in mathematics and economics from the University of British Columbia in his first job as a data scientist working for a Vancouver tech company.
Having an advanced degree is often necessary because even if you majored in computer science, “you probably won’t know much about machine learning.” That knowledge tends to come through masters- and PhD-level classes and “hasn’t trickled down through to the undergraduate level yet,” Fraser said.
Still, students can supplement their university education with AI-focused classes offered through online learning platforms like Coursera, Udemy and fast.ai. Those courses are often very well-regarded among employers, Fraser said.
“People put them on their resume.”
With a few online classes and some solid data science experience earned through internships, tech grads can get data science jobs without an advance degree, Fraser said.
Data engineer/data warehouse engineer. Data is often messy and hard to access. If you wanted to analyze consumer spending patterns, for example, you need to know when they shopped, what they bought, their age and their gender. Each one of those data sets may be stored in a different database and format. That’s where data engineers come in, Fraser said. Their job is to provide data scientists with the building blocks they need to build their predictive models.
Data engineering jobs usually require a degree in computer science or software engineering. The average salary on Indeed was nearly $86,000.
Machine learning engineer. When it comes to AI algorithms, data scientists are more like product designers, Fraser said. “They come up with the idea.” Machine learning engineers are in charge of tweaking those algorithms to ensure optimal performance, he added.
The job usually requires an advanced degree.
Top tech grads aren’t just nailing a big salary as soon as their enter the job market. They’re also paying the bills while they’re in school.
Just ask Lisa Cooper. The University of Waterloo alum graduated in May with a degree in mecatronics engineering and, she said, not a single penny in student debt.
Not only that, but she also has savings “in the high five-figures,” with which she’s hoping to buy a house before too long, she told Global News.
The money comes from her co-op work, including two stints in Silicon Valley that earned her $100,000 over the course of eight months. Overall, those paycheques were enough to cover her $32,500 tuition costs not covered by scholarships and other benefits, as well as around the $35,000 in living expenses she estimates she spent through university.
Once school is over, those who secure a job in data analytics or AI can look forward to work-life perks like free fitness and health classes, paid-for office meals, standing desks, plenty of work-from-home flexibility, and even “bring your dog to work” days, Ward said.
And, he added, within a few years, they’ll advance to senior-level positions, which pay in the $100,000-$150,000 range, sometimes with signing bonuses of between $10,000 and $50,000.
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A six-figure job definitely has its downsides.
Gigs that comes with astronomical salaries usually require very long hours, Smith warned.
“What young people tend not to do is calculate the wage per hour worked.”
Instead, he added, every job candidate should “insist that [they] be told what the typical workweek looks like.”
For example, a $100,000 paycheque works out to just $32 an hour before tax if you’re putting in 60 hours per week. That compares to $48 an hour before tax for a 40-hour-a-week job.
Cooper, for one, said one of her Silicon Valley internships involved 15-hour days and often required work on weekends.
“California was a great experience,” she said. “But it wasn’t a lifestyle that I found sustainable.”
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Another thing for new grads to watch out for is being asked to accept equity in exchange for lower-than-average pay. While company shares are a standard part of compensation in the tech industry, stocks “should be no reason to accept gross underpaying,” Smith said.
Especially if you work at a startup, there is no guarantee that your equity will be worth anything, Ward said.
And sometimes even a workplace benefit could turn out to be anything but.
Take unlimited vacation, for example. “People love the concept,” said Ward. What happens, though, is that they tend to take less time off.
In an office where no one has a set number of weeks earmarked for down times, there is “no guarantee that a promotion won’t disappear because you took vacation,” Smith said.
But the most important cautionary note about data analytics and AI jobs is that tech job hiring sprees tend to come and go.
“The tech industry has these constant enthusiasms,” Smith said. “AI is the flavour of the month.”
Something similar happened in the early 2000s with web designers, he recalled.
Back then, “every company needed a website,” he said. And while web designers are still in demand, the hiring surge is a thing of the past.
One day, “the frenzy for AI will also abate.”
Still, for the next several years at least, the future looks bright.
By 2030, some 70 percent of companies might have adopted some kind of AI technology, a shift that could grow the global economy by as much as $13 trillion by 2030, according to McKinsey Global Institute.
In Canada, there will be some 216,000 jobs in information and communications technology that will need to be filled by 2021, estimates the ICTC.
Many of those new openings will be related to AI and blockchain.