Canada’s Data Scientist career does have immense scope.
A data scientist uses data to assist businesses in making wise decisions. Data scientists are employed in sectors like healthcare, banking, transportation, and e-commerce. In Canada, a data scientist makes an average yearly salary of 121,000 CAD (72.9 lakhs INR).
Data scientists are in demand, and the market is expanding. More businesses are now offering competitive pay and perks. Also, there are more options for data scientists to find employment in this country. Understanding statistics, scientific methods, and algorithms are necessary for data science. This blog post will examine the Data Scientist salary in Canada as well as various job types, essential abilities, and other important information.
Canada’s Data Scientist career – what is the scope?
In comparison to many other nations, Canada pays a data scientist a greater wage. Data scientists typically start with a yearly salary of $60,000. Those experiences can make over $100,000 annually. Canada strongly emphasizes innovation and technology, and as a result, many technology-driven businesses have emerged. Consequently, there is a lot of room for data professionals in the Canadian labor market. Government agencies and private businesses are in great need of people with degrees in data science. Data science has expanded as a result of the increased usage of big data, machine learning, and artificial intelligence. According to IBM, by 2022, Canada’s need for data scientists has the potential to grow by 28%.
Canada’s Data Scientist career – which industries provide higher incomes?
Large MNCs and organizations find Canada to be a lucrative market. Take a look at the top-paying industries for data scientists.
- Education: $80,000 – $110,000
- Telecommunications: $90,000 – $140,000
- Finance: $90,000 – $130,000
- Transportation: $80,000 – $120,000
- Healthcare: $85,000 – $120,000
- E-commerce: $90,000 – $130,000
- Technology: $90,000 – $140,000
- Consulting firms: $90,000 – $140,000
- Energy: $90,000 – $130,000
- Retail: $80,000 – $110,000
Which cities pay the highest to Data Scientists in Canada?
- Waterloo, ON – $105,603 per year
- Toronto, ON – $92,795 per year
- Vancouver, BC – $95,772 per year
- Calgary, AB – $94,004 per year
- Mississauga, ON – $88,552 per year
- Edmonton, AB – $81,269 per year
- Ottawa, ON – $89,009 per year
- Montréal, QC – $83,236 per year
- Brampton, ON – $75,096 per year
The top-paying Data Scientist careers in Canada
| Job Titles | Job Description | Average Salary (in CAD) |
| Data Scientist | Make data-driven decisions by analyzing and interpreting complex data to glean insights. | 121,631 |
| Business Intelligence Analyst | Gather, research, and interpret data to offer perceptions and suggestions that assist in business decisions. | 77,630 |
| Big Data Architect | Large-scale data solutions that satisfy business needs and facilitate data analysis and decision-making are designed and managed during installation. | 85,000 |
| Artificial Intelligence (AI) Developer | Create, design, and use algorithms and models to give robots the ability to do tasks and reach judgments. | 90,000 |
| Business Intelligence Developer | Develop, create, and maintain business information solutions that aid decision-making and offer insights. Knowledge of software engineering is required. | 82,000 |
| Application Architect | Software applications should be designed and developed while considering the organization’s business needs. | 100,000 |
| Data Engineer | To enable efficient and effective data processing and retrieval, design, implement, and maintain data pipelines and infrastructure. | 131,120 |
| Business Analyst | Develop solutions to increase organizational efficiency and effectiveness by analyzing and evaluating business processes, and identifying opportunities for improvement. | 79,500 |
| Machine learning scientist | Create, and use machine learning models and algorithms to examine and glean insights from data. | 160,000 |
| Data Analyst | Large datasets should be gathered, processed, and statistical analysis should be done to offer information for business choices. | 86,956 |
| Data Mining Engineer | Produce and execute methods and procedures to glean knowledge and insights from huge datasets. | 81,000 |
Salary of Data Scientists based on experience
- CAD $80,000 – $100,000 per year – Mid Career (2-5 years)
- Entry-Level (0-2 years): CAD $60,000 – $80,000 annually.
- CAD $100,000 – $150,000 per year – Senior/Experienced (Above five years)
Prominent firms recruiting Canada’s Data Scientists
| Company | Average Salary |
| Technical Safety BC | $124,841 per year |
| Yelp | $121,812 per year |
| Shopify | $119,658 per year |
| RBC | $100,435 per year |
| Shopify | $114,139 per year |
| Scotiabank | $105,307 per year |
| Deloitte | $60,000 per year |
| Ubisoft Entertainment Inc. | $73,986 per year |
| Manulife | $63,186 per year |
| Capital One Canada | $75,000 per year |
Data Scientist’s must-have skills
Maths and Science
The algorithms and models used in data science are built on mathematical and statistical principles. These abilities aid in the analysis of facts and the production of insightful conclusions. They are necessary for data scientists to create and test hypotheses, make predictions, and identify uncertainty. Additionally, learning machine learning is necessary. One of the top abilities of a data scientist is calculus.
Programming
A key component of careers in data science involves programming. A data scientist must have programming language proficiency. Programming languages, including Java, Python, Hadoop, SQL, and C++, should be known to data scientists. The creation and use of sophisticated algorithms and models require programming. Your ability to speak various languages may open up higher-paying job opportunities for you.
Machine Learning and AI
These fields truly count as different specializations despite the fact that any data scientist should be familiar with the fundamental ideas of machine learning, deep learning, and AI. These topics do cross over. Data science uses a variety of deep learning and machine learning models, such as decision trees and prediction models, to mine data. Therefore, machine learning needs data provided by data science to train its algorithms.
Data Visualization
It is crucial for conveying the insights you’ve discovered as a data scientist. In essence, information is made easier for us to understand through the process of transforming data into tables, pie charts, bar charts, scatter plots, heat maps, and other visualizations.
Various visualization tools, such as Tableau or directly constructing visualizations in Python, can be used to visualize data. As important in a data scientist’s work as finding insights is uncovering them. Therefore presentation and visualization skills are often included at data science boot camps.
Cloud Computing
The information used by data scientists isn’t kept on the computer in front of them. Big data is instead frequently stored via cloud computing. Therefore, being able to communicate with the cloud and comprehend its fundamental workings might be a useful ability for data scientists.
