DataScienceJobsCanada.com presents:
Data Scientist Salary in Canada (July 2026)
Overview of Data Scientist Salary in Canada
In this guide we will present and analyze the data scientist salary Canada offers. We discuss data scientist salary and effective salary in major cities like Toronto, Ontario, Vancouver, Montreal, and throughout the rest of the country. You will also find tips to optimize your income in Canada as a data scientist or machine learning engineer.
Click the links below to jump to each section.
- Data Scientist Salary Distribution in Canada
- Median Data Scientist Salary by Region
- Median Salary, Adjusted by Cost of Living
- Top Paying Companies in Canada
- Distribution of Jobs Across Canada
- How to Research Salary
- What Goes Into a Salary?
- How to Make More Money
- Next Steps
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Data Scientist Salary Distribution in Canada
The median data scientist salary in Canada is $130K, with the top percentiles approaching $400K! This data includes a range of experience levels, so you may find yourself drifting up the salary percentiles over time. The highest paid positions can also require specialized training such as a PhD. Continuously learn to level up and build your career!
Median Data Scientist Salary by Region
Toronto, Vancouver and Montreal are three major technology, data science and machine learning hubs in Canada. Toronto is the largest, with the most data points available, while the data from Vancouver and Montreal is noisier given smaller sample sizes. Additional Canadian cities were excluded due to lower quantities of data available. Note that equity, when offered, can be a major source of income for competitive compensation packages, though job postings rarely disclose it.
Data scientist salary in Toronto, Ontario is a median of $130k. Vancouver data scientist salary has the highest median of the major cities, at $140k, though its smaller sample size makes that figure noisier. The lowest median is data scientist salaries in Montreal, Quebec, at $104k. These figures are based on advertised salaries from job postings, which typically reflect base pay and rarely break out equity or bonus separately.
It remains to be seen if remote work will level these discrepancies over time. Some employers, like Google, pay remote employees different rates based on the city that they live in. With a competitive job market, it will be interesting so see if this type of practice lasts.
Median Salary, Adjusted by Cost of Living
It can be important to consider the cost of a particular city when assessing the salaries in the area. This chart shows the compensation shown above, adjusted by the approximate cost of living for an adult couple living in each city.
The cost of living for Toronto is estimated as $45K, Vancouver as $36K, and Montreal as $24K. The cost of living makes Vancouver an even more appealing choice, but Toronto's take home still appears to be more than Montreal. For additional cost of living calculation, sites like WOWA can be useful.
Adjusted for cost of living, the median data scientist salary in Toronto is $85k. Vancouver still has the highest median salary after the adjustment, at $104k. The median salary for data scientists adjusted for cost of living in Montreal is the lowest, at $80k.
Additionally, you may be interested in the aggregate tax rates for each province as this will also impact earnings in different provinces. TaxTips.ca has useful charts for comparing taxes for different earning levels in different provinces.
Top Paying Data Scientist Companies in Canada
| Rank | Company | Location | High Percentile (CAD) |
|---|---|---|---|
| #1 | Borealis AI | Toronto, Montreal, Waterloo, Vancouver | $430K |
| #2 | Amazon | Toronto, Vancouver | $371K |
| #3 | Instacart | Remote, Toronto | $365K |
| #4 | Microsoft | Toronto, Vancouver | $356K |
| #5 | Meta | Toronto, Vancouver, Montreal | $340K |
| #6 | RBC | Toronto | $241K |
These are the highest individual data scientist packages reported in Canada, so they sit well above typical pay. Notably, the top spot now goes to RBC's research lab, Borealis AI, with Instacart and Amazon close behind — you don't have to be at a FAANG to land a top-tier package. The scale of "FAANG" can still mean more openings if you can conquer their challenging interview processes, and these figures are most representative of senior data scientist compensation in Canada.
With remote work, you may be able to optimize your cost of living to increase your take home pay. If you're looking to increase your salary, consider US based companies that offer remote work in Canada, because both their salary standards and the USD to CAD exchange rate will favour you.
Distribution of Data Scientist Jobs Across Canada
Toronto is the largest tech hub in Canada, and unsurprisingly has the vast majority of data scientist job postings. Vancouver and Montreal are secondary technology centers, with Calgary a distant fourth. Other cities across the countries still have jobs available, but they are few in number.
Remote jobs offer the possibility to work from anywhere, so more than ever before are data scientists and engineers not necessarily limited to the locations captured in this figure.
Data obtained from various aggregate websites.
How to Research Salary
Researching salaries for similar positions in your region is crucial in determining what you can expect to make. Understanding the job market for data science is also important when negotiating your offers. It is particularly important to get accurate data in the technology industry: unfortunately, many sites list compensations that are much lower than the true market rates. Know your worth!
For example, here is a summary of different salary ranges for data scientist jobs in Canada from popular websites:
| Source | Low %ile | Median | High %ile |
|---|---|---|---|
| Glassdoor | $82K | $102K | $128K |
| Indeed | N/A | $101K | N/A |
| levels.fyi | $100K | $125K | $152K |
| payscale | $63K | $89K | $118K |
| talent.com | $137K | $148K | $163K |
levels.fyi remains one of the most reliable sources for verified technology salaries because every submission requires a proof document. As of 2026, headline ranges across sites have compressed and converged: levels.fyi, talent.com and our own job-market data now land in a similar band, while some sources still trend lower. Cross-reference a few verified sources to triangulate an accurate number for the companies you are targeting.
The other websites use a combination of unverified anonymous salary submissions and salaries taken from job postings, so their figures can vary widely from what is actually offered in the job market — some run markedly lower, and occasionally higher. Companies will happily lowball you, which is why it's important to get accurate data! With remote work on the rise, it may be beneficial to look at salaries in other regions as well. Use levels.fyi to assess companies you are interested in and the types of offers you might receive.
What Goes Into a Salary?
A more wholistic view of your salary is your total compensation. Total compensation is an aggregate of everything the company is providing you. It is easier to assess and compare salaries by looking at all pieces of the compensation package.For data science and other technology jobs, components of your offer may include:
- Base salary
- Equity
- Yearly bonus
- Benefits
- Signing bonus
Startups will frequently offer more equity in the form of less reliable stock options, whereas more established institutions prefer a higher base salary and bonuses. FAANG is in a league of its own and often offers market leading base salary, bonus and stock packages.
When considering equity, there will be uncertainty. Equity is not always guaranteed, and can change in value depending on the type of equity that is being offered. For example, stock options have an expiry and are only worth something if the stock price of a company exceeds a certain value. Typically, this means that the stock options that you are provided will only be worth something if the company grows as a certain pace to achieve higher valuations.
On the other hand, restricted stock units (RSUs) may be safer in comparison. A company that is already public has less of a chance of going to zero than a start-up, but there is still risk they will be worth less than you expect. Of course, with both these options, they could also be worth more in the best case scenarios! That’s why it’s important to understand the range of compensation that equity could provide, and understand the possible risks to the value you receive. Equity is a less reliable form of income than base salary, but there is a higher possible upside. For more information, here is a thorough "crash course" article on equity compensation at tech companies.
Yearly bonuses can be based on performance and also may have a range of possibilities. Benefits can have different value to different people: some people are willing to take a salary cut to get another 2 weeks of vacation each year. To assess this, you need to understand your risk tolerance and the components of the compensation that are the most important to you.
Benefits are also a major consideration. Vacation time can be more meaningful to different people, as can the general work life balance of different organizations. Consider how much vacation time and non-monetary benefits are worth to you, to aid in comparing potential opportunities.
How to Make More Money
The approach to getting paid more in the tech industry is a big topic, and there is more than one path to success. Here a few quick tips to get you started!
Ways to improve your pay:
- Negotiate: Optimize your offer
- Find a new job: Keep up with market rates
- Get promoted: Achieve your worth
- Do not give recruiters or companies your target salary.
- When offered a salary, counter offer based on your research (or 5-20% more).
- Kalzumeus' Salary Negotiation: Make More Money, Be More Valued
- Exponent's Negotiate Your Tech Offer
When applying to jobs at another company, from the company's perspective, there is risk in a new hire. Therefore, they want to do what they can to ensure they hire exceptional candidates who are capable of excelling at their tasks. As such, is often more reliable to get offers for positions that you are doing: this directly demonstrates to the company that you understand and can execute on the particular set of responsibilities. Personal and professional connections can also be very powerful in convincing a company to hire you. This is another piece of evidence that can be used to validate that you are the real deal.
It is still possible to move upwards when finding new jobs. Identifying key learnings from your work history, discussing your ongoing training and education, and understanding your work and personal projects deeply can provide signals for company to understand your capabilities and justify hiring. As part of this, try to highlight responsibilities or proven experience in areas that the position requires. For example, if you are a Data Scientist, and are hoping to become a Senior Data Scientist, it can be helpful to have examples of times you’ve mentored others on your team, since seniors are usually responsible for growing other team members as well.
Good luck, and get paid! 💰
Next Steps
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