Research Scientist Salary in Canada during 2024
💰 The median Research Scientist Salary in Canada during 2024 is USD 111,000
✏️ This salary info is based on 24 individual salaries reported during 2024
Salary details
The average Research Scientist salary lies between USD 96,643 and USD 158,000 in Canada. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.
- Job title
- Research Scientist
- Experience
- all levels
- Region
- Canada
- Salary year
- 2024
- Sample size
- 24
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.
Last updated:Top 20 Job Tags for Research Scientist roles
The three most common job tag items assiciated with Research Scientist job listings are Research, Machine Learning and Python. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:
Research | 2076 jobs Machine Learning | 1692 jobs Python | 1554 jobs PhD | 1330 jobs Computer Science | 1324 jobs Engineering | 1206 jobs Deep Learning | 956 jobs PyTorch | 869 jobs NLP | 820 jobs Computer Vision | 753 jobs Physics | 745 jobs NeurIPS | 701 jobs LLMs | 673 jobs Statistics | 609 jobs ICLR | 593 jobs Mathematics | 585 jobs ICML | 583 jobs TensorFlow | 564 jobs Architecture | 532 jobs VR | 529 jobsTop 20 Job Perks/Benefits for Research Scientist roles
The three most common job benefits and perks assiciated with Research Scientist job listings are Career development, Health care and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:
Career development | 1678 jobs Health care | 1093 jobs Equity / stock options | 1091 jobs Conferences | 1037 jobs Salary bonus | 674 jobs Flex vacation | 409 jobs Medical leave | 392 jobs Insurance | 375 jobs Parental leave | 366 jobs Flex hours | 355 jobs Competitive pay | 335 jobs 401(k) matching | 321 jobs Startup environment | 292 jobs Team events | 253 jobs Flexible spending account | 191 jobs Wellness | 183 jobs Transparency | 89 jobs Relocation support | 87 jobs Unlimited paid time off | 50 jobs Fitness / gym | 44 jobsSalary Composition for AI/ML/Data Science Research Scientist Roles
In Canada, the salary composition for AI/ML/Data Science Research Scientists can vary significantly based on factors such as region, industry, and company size. Typically, the salary package is composed of:
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Base Salary: This is the fixed component and usually constitutes the majority of the total compensation. In tech hubs like Toronto or Vancouver, the base salary might be higher compared to other regions due to the cost of living and demand for talent.
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Bonus: Many companies offer performance-based bonuses. These can be annual or quarterly and are often tied to individual, team, or company performance metrics. The bonus can range from 5% to 20% of the base salary, depending on the company and industry.
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Equity or Stock Options: Particularly in startups or tech companies, equity can be a significant part of the compensation package. This offers potential long-term financial benefits if the company performs well.
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Other Benefits: These might include health insurance, retirement contributions, and other perks like professional development funds, which can add substantial value to the overall compensation package.
Steps to Increase Salary from This Position
To increase your salary beyond the median of USD 117,000, consider the following strategies:
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Specialize in High-Demand Areas: Focus on niche areas within AI/ML, such as deep learning, natural language processing, or computer vision, which are in high demand and can command higher salaries.
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Pursue Leadership Roles: Transitioning into a lead or managerial role can significantly increase your earning potential. This might involve managing a team of data scientists or leading a project.
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Continuous Learning and Upskilling: Stay updated with the latest technologies and methodologies in AI/ML. Attending workshops, conferences, and pursuing advanced certifications can enhance your skill set and marketability.
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Negotiate Effectively: When offered a new position or during performance reviews, negotiate your salary and benefits package. Research industry standards and be prepared to articulate your value to the organization.
Educational Requirements
Most AI/ML/Data Science Research Scientist positions require:
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Advanced Degrees: A Master's degree in Computer Science, Data Science, Statistics, or a related field is often the minimum requirement. A Ph.D. is highly preferred, especially for research-intensive roles, as it demonstrates a deep understanding of complex algorithms and methodologies.
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Strong Mathematical Foundation: Proficiency in mathematics, particularly in areas like linear algebra, calculus, and probability, is crucial for developing and understanding machine learning models.
Helpful Certifications
While not always mandatory, certain certifications can enhance your profile:
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Certified Machine Learning Professional (CMLP): This certification demonstrates a comprehensive understanding of machine learning concepts and applications.
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TensorFlow Developer Certificate: This is beneficial for those working with TensorFlow, a popular machine learning framework.
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AWS Certified Machine Learning – Specialty: This certification is valuable for roles involving cloud-based machine learning solutions.
Required Experience
Typically, employers look for:
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Relevant Work Experience: At least 3-5 years of experience in data science or machine learning roles. Experience in research, either in academia or industry, is highly valued.
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Project Experience: Demonstrated experience in handling end-to-end machine learning projects, from data collection and preprocessing to model deployment and evaluation.
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