Salary for Mid-level / Intermediate Data Scientist in Canada during 2024

πŸ’° The median Salary for Mid-level / Intermediate Data Scientist in Canada during 2024 is USD 105,368

✏️ This salary info is based on 90 individual salaries reported during 2024

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Salary details

The average mid-level / intermediate Data Scientist salary lies between USD 85,000 and USD 137,930 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
Data Scientist
Experience
Mid-level / Intermediate
Region
Canada
Salary year
2024
Sample size
90
Top 10%
$ 162,500
Top 25%
$ 137,930
Median
$ 105,368
Bottom 25%
$ 85,000
Bottom 10%
$ 59,570

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:

Salary trend

Top 20 Job Tags for Mid-level / Intermediate Data Scientist roles

The three most common job tag items assiciated with mid-level / intermediate Data Scientist job listings are Python, Statistics and Machine Learning. 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:

Python | 3142 jobs Statistics | 2773 jobs Machine Learning | 2745 jobs SQL | 2425 jobs Engineering | 2162 jobs Computer Science | 1869 jobs R | 1861 jobs Mathematics | 1648 jobs Research | 1521 jobs Data analysis | 1261 jobs ML models | 1083 jobs Testing | 918 jobs AWS | 916 jobs Data visualization | 872 jobs Tableau | 793 jobs Pipelines | 767 jobs Spark | 733 jobs NLP | 718 jobs Big Data | 674 jobs Security | 673 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Data Scientist roles

The three most common job benefits and perks assiciated with mid-level / intermediate Data 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 | 2576 jobs Health care | 1342 jobs Equity / stock options | 893 jobs Flex hours | 885 jobs Competitive pay | 675 jobs Insurance | 629 jobs Startup environment | 619 jobs Medical leave | 617 jobs Parental leave | 570 jobs Team events | 539 jobs Salary bonus | 527 jobs Flex vacation | 515 jobs 401(k) matching | 400 jobs Wellness | 348 jobs Conferences | 159 jobs Unlimited paid time off | 140 jobs Flexible spending account | 136 jobs Relocation support | 119 jobs Fitness / gym | 113 jobs Transparency | 113 jobs

Salary Composition for Mid-level Data Scientist Roles in Canada

The salary for a mid-level data scientist in Canada typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company’s performance and individual achievements, often ranging from 5% to 15% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and comprehensive benefits packages that cover health insurance, retirement plans, and other perks. The composition can vary by region, with tech hubs like Toronto and Vancouver offering higher base salaries and bonuses compared to smaller cities. Industry also plays a role; for instance, data scientists in finance or tech sectors might receive higher bonuses compared to those in academia or public sectors. Larger companies often provide more structured bonus schemes and additional benefits compared to smaller firms.

Steps to Increase Salary from a Mid-level Position

To increase your salary from a mid-level data scientist position, consider the following strategies:

  • Skill Enhancement: Continuously update your skills in emerging technologies such as deep learning, natural language processing, or cloud computing. Mastery in these areas can make you more valuable to employers.
  • Advanced Education: Pursuing a master's degree or a Ph.D. in data science or a related field can open doors to higher-paying roles.
  • Leadership Roles: Transitioning into roles that involve team leadership or project management can lead to salary increases.
  • Industry Specialization: Specializing in high-demand industries like finance, healthcare, or AI can lead to higher compensation.
  • Networking and Professional Visibility: Attend industry conferences, contribute to open-source projects, or publish research to increase your visibility and attract better job offers.

Educational Requirements for Mid-level Data Scientist Roles

Most mid-level data scientist positions require at least a bachelor's degree in a relevant field such as computer science, statistics, mathematics, or engineering. However, a master's degree is often preferred and can be a significant advantage. Some roles may also consider candidates with degrees in other quantitative fields if they have relevant experience or skills. A strong foundation in statistical analysis, machine learning, and programming is essential.

Helpful Certifications for Data Scientists

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise:

  • Certified Data Scientist (CDS)
  • Microsoft Certified: Azure Data Scientist Associate
  • Google Professional Data Engineer
  • IBM Data Science Professional Certificate
  • AWS Certified Machine Learning – Specialty

These certifications can validate your skills in specific tools and platforms, making you more attractive to potential employers.

Experience Requirements for Mid-level Data Scientist Roles

Typically, mid-level data scientist roles require 3 to 5 years of relevant experience. This experience should include hands-on work with data analysis, machine learning model development, and data visualization. Experience in handling large datasets, proficiency in programming languages like Python or R, and familiarity with data manipulation tools such as SQL are often expected. Experience in a specific industry can also be beneficial, as it provides domain knowledge that can be crucial for certain roles.

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