Data Scientist vs Computer Vision Engineer: Which Pays More?
Side-by-side salary comparison by city, experience level, and career growth outlook. Data reflects current market rates.
Data Scientist
Analyse large datasets and build predictive models to drive decisions.
Computer Vision Engineer
Build image and video recognition systems using deep learning.
Data Scientist earns more on average — the national median is $0/year (0%) higher than a Computer Vision Engineer. However, salaries vary significantly by city, employer, and experience level — see the city-by-city breakdown below.
Data Scientist vs Computer Vision Engineer — Salary by City
National median figures in USD across top cities.
| City | Data Scientist | Computer Vision Engineer | Difference |
|---|---|---|---|
| San Francisco, CA | $176,777 | $175,639 | Data +$1,138 |
| New York, NY | $135,980 | $135,980 | Data +$0 |
| Seattle, WA | $173,186 | $171,965 | Data +$1,221 |
| Austin, TX | $130,733 | $137,560 | Data $6,827 |
| Chicago, IL | $132,415 | $126,140 | Data +$6,275 |
| Boston, MA | $156,115 | $155,915 | Data +$200 |
| London, UK | £78,216 | £95,414 | Data $17,198 |
| Toronto, Canada | CA$134,265 | CA$156,431 | Data $22,166 |
Frequently Asked Questions
Does a Data Scientist or Computer Vision Engineer earn more?+
A Data Scientist earns more on average. The national median salary for a Data Scientist is $120,230/year, compared to $120,230/year for a Computer Vision Engineer — a difference of $0 (0%).
Which has better career growth — Data Scientist or Computer Vision Engineer?+
Data Scientist roles are growing at 36% YoY while Computer Vision Engineer demand is growing at 38% YoY. Computer Vision Engineer has stronger near-term demand growth.
Can you switch from Data Scientist to Computer Vision Engineer?+
Yes. Many professionals transition between these roles, especially since both are in the same category. Shared skills include: Python, TensorFlow.
Which is harder to automate — Data Scientist or Computer Vision Engineer?+
Computer Vision Engineer has a lower AI automation risk (22% vs 40%). Based on Oxford Martin School and McKinsey 2023 analysis.