Data scientist salary
Percentile salary data for data scientist roles in Toronto, CA, by seniority. Senior-level figures include total compensation where regionally applicable. Figures are shown in USD by default, with the local CAD amount alongside; use the currency switcher to change the display.
Toronto carries a cost-of-living index of 74 on the Numbeo and Mercer blended scale where New York City equals 100, a moderate cost of living that is below New York City. The figures below are nominal pay in CAD; read them alongside that index when comparing Toronto with other markets.
Median pay by seniority
Junior
US$51,830 (local: $71,000)
Base p50
Mid
US$79,570 (local: $109,000)
Base p50
Senior
US$115,340 (local: $158,000)
Base p50
Staff
US$294,190 (local: $403,000)
Total comp p50
Distribution
10th, 25th, 50th, 75th, and 90th percentile by seniority, shown in the local currency (CAD). The detailed table below defaults to USD.
Salary percentiles by seniority, in CAD. Junior: 10th percentile CA$46,000, 25th CA$58,000, median CA$71,000, 75th CA$89,000, 90th percentile CA$110,000. Mid: 10th percentile CA$71,000, 25th CA$90,000, median CA$109,000, 75th CA$137,000, 90th percentile CA$169,000. Senior: 10th percentile CA$103,000, 25th CA$130,000, median CA$158,000, 75th CA$198,000, 90th percentile CA$246,000. Staff: 10th percentile CA$290,000, 25th CA$347,000, median CA$403,000, 75th CA$544,000, 90th percentile CA$726,000.
| Seniority | 10th | 25th | Median | 75th | 90th | Comp | Sample |
|---|---|---|---|---|---|---|---|
| junior | US$33,580 | US$42,340 | US$51,830 | US$64,970 | US$80,300 | Base | 320 |
| mid | US$51,830 | US$65,700 | US$79,570 | US$100,010 | US$123,370 | Base | 480 |
| senior | US$75,190 | US$94,900 | US$115,340 | US$144,540 | US$179,580 | Base | 220 |
| staff | US$211,700 | US$253,310 | US$294,190 | US$397,120 | US$529,980 | Total comp | Not reported |
Source: US BLS OES May 2024 (SOC 15-2051) + Levels.fyi 2025 (2025) https://www.bls.gov/oes/current/oes152051.htm, cost-of-living index 74 (NYC=100)
Each step compares the median (50th percentile) of one tier with the next, using the figures in the table above for data scientist roles in Toronto.
Take-home
Headline salaries are gross. What lands in your account after income tax and mandatory contributions is what actually pays the rent, so here is the estimated split on the Toronto senior median.
Gross / year
$158,000
Senior median (50th pct)
Take-home / year
$110,887
70% of gross
Take-home / month
$9,241
from $13,167 gross
Effective rate
30%
tax + contributions
On a senior median of $158,000 in Toronto, an estimated $47,113 goes to income tax and mandatory contributions, leaving roughly $110,887 take-home, or about 70% of gross.
That deduction breaks down as roughly $27,779 in income tax, $4,877 in social contributions, and $14,457 in state/provincial income tax (ontario) at this income level in Canada.
In monthly terms that is about $9,241 net from $13,167 gross a month, the figure that actually lands in a data scientist's account before pension or benefit elections.
Affordability
"Good" depends on what the city costs. Type your own figure below to see the estimated take-home and how far it goes against Toronto's single-person cost of living, or read the verdict on the senior median first.
Enter an annual gross figure to see the estimated take-home and how it compares with Toronto's single-person cost of living. Estimate based on Canada CRA 2025 federal + provincial + CPP/EI.
Take-home / year
$110,887
70% of gross
Take-home / month
$9,241
from $13,167 gross
Est. monthly living cost
$5,778
incl. ~$4,258 rent
Left over / month
$3,463
37% of take-home
manageable in Toronto
On $158,000 gross, your estimated take-home covers typical single-person living costs with a modest margin left, with rent taking a larger share of take-home than the 30% guideline. Rent is about 46% of monthly take-home here.
Reference estimate for a single filer with no dependants, using published 2025/2026 statutory bands and a cost-of-living index where New York City equals 100. It is not tax advice or a personalised calculation. Real take-home and living costs vary with filing status, pension elections, allowances, and where exactly you live.
Against Toronto's single-person cost base, a senior data scientist on this median is manageable: the estimated take-home of $9,241 a month covers typical single-person living costs with a modest margin left, with rent taking a larger share of take-home than the 30% guideline.
Typical single-person monthly costs in Toronto, including roughly $4,258 for a one-bedroom rental, come to about $5,778, which would leave around $3,463 discretionary each month at the median.
Rent works out at about 46% of monthly take-home here, against the conventional 30% guideline often used as a reference point.
The verdict is descriptive, not advice: it compares one number against a modelled cost base. Living costs and tax both vary with circumstances this page cannot see.
Why the same job title in Toronto can span the spread shown above. These are descriptions of the data, not guidance on what to ask for.
Toronto has a cost of living about 26% lower than New York City. Because the two report in different currencies, compare the percentile tables directly rather than the headline numbers.
Real-terms figures divide the nominal median by the city cost-of-living index (New York City = 100). They are a rough purchasing-power comparison, not a take-home or after-tax calculation.
The mid-level median anchors to occupation medians from the sources below, then scales by the documented seniority multipliers (junior 0.65x, mid 1.00x, senior 1.45x, staff 2.05x) and the local tech-pay level for Toronto. The 10th, 25th, 75th, and 90th percentiles are fitted from the median using the cross-sectional dispersion that the US Bureau of Labor Statistics reports for software occupations. Cost-of-living indices blend Numbeo 2025 and Mercer 2025.
Primary source for this slice: US BLS OES May 2024 (SOC 15-2051) + Levels.fyi 2025 (2025). https://www.bls.gov/oes/current/oes152051.htm
Reference data only. Reported medians describe a market; individual pay varies by employer, level calibration, and negotiation. Nothing here is financial advice.
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Compare data scientist pay with adjacent roles in the same market.