Data scientist. It’s one of those technology jobs that sounds super-technical, a bit mysterious and, well, hard to get. However, landing jobs in data science, particularly at the entry level, may not be as insurmountable as you think.
First, know that there is demand for data science talent in Canada. The latest Salary Guide From Robert Half reports that recruiting for the tech sector is especially active, as employers are hiring technology professionals at or beyond pre-pandemic levels.
As businesses accelerate their digital transformation, data scientists are needed across all major business sectors — from technology and manufacturing to financial services and healthcare — as well as organizations in academia, government and the nonprofit sector. That’s because organizations of all types need to turn numbers into recommended strategies and actions.
Unlocking the value of big data
As the world becomes increasingly data-driven, data gets more and more valuable — provided it can be put to practical business use. This is where the data scientist comes in. Businesses need people with knowledge of statistics and data modeling to unlock the value of complex, unprocessed data from an array of different sources — machine log data, digital media and documents, databases, the web, social media channels and Internet of Things (IoT) sensors.
The business intelligence, or actionable insights, that companies can glean from the data they gather can be used to inform decisions about everything from new product development to marketing campaigns to supply chain design. Organizations are also relying more on these insights to help them improve cybersecurity, employee retention, recruitment and productivity, customer service and engagement, and much more.
Because companies can use data-driven business intelligence in so many ways, they want to hire data scientists who have a head for business. Communication and other soft skills are also essential. One reason these skills are so important is that data scientists are often required to explain, quickly and concisely, to nontechnical people the risks, trends and opportunities that the business should monitor or act on.
Data scientists are often expected to describe their analysis in writing or present their findings directly to business teams. And collaboration skills are becoming increasingly important for this role, too.
Diverse technical skills required
Of course, there are technical skills required in this role. Data scientists need to bring a range of analytical and mathematical know-how to their roles — not just any old math, but areas like multivariable calculus and linear algebra. Data science is essentially a blend of statistics and mathematics and computer science. So, many employers look specifically for candidates with expertise in statistics. Machine learning skills are also valued because they help data scientists identify patterns in data.
Experience working with programming languages, such as Python (a flexible language that’s generally easy to use) or Java (one of the oldest languages and applicable to nearly every area of technology), is often part of the data scientist job description. Many businesses also seek professionals who can work with languages like R, which is used for statistical analysis, data visualization and predictive modeling, and with tools like Tableau for interactive data visualization.
Data scientists with skills in Apache Hadoop, Microsoft SQL Server database and Oracle database, and Extract, Transform, Load (ETL), like database schema design and systems building, are also highly valued.
Education level key for advanced data scientist roles
Many organizations prefer to hire data scientists who have earned a Ph.D. in a related area like mathematics or computer science. A doctorate can provide candidates an edge in the hiring process, and it’s an absolute requirement for some roles. While an advanced degree may not be essential to get hired for an entry-level data scientist role, it is likely to become more important as you look to advance in your data science career.
Data scientist salary: what to expect
Now that you have a better sense of the soft skills, technical abilities and education requirements needed for a data science career, what type of data science salary might you expect? You can find the latest salary midpoint (or median national salary) for data scientists in the Robert Half Salary Guide.
Note that salary rates differ by experience and location. To localize your insights for a data scientist salary for your city, use the tools you can access in the Salary Guide.
Laying the groundwork for a data science career
If you’re a university student or recent graduate wondering how to become a data scientist, must-have job requirements will depend largely on the employer, what technology tools the company uses for managing its data, and whether the business has the time and resources to invest in developing entry-level data scientists.
Here are some ways to gain relevant knowledge and skills and increase your chances of successfully launching a data science career:
Stay current with online resources
Look for e-books, online courses and video tutorials that dig deeper into online data analysis, statistics, data coding and related topics that interest you. (Just some examples of resources offering online learning options for data science include Coursera, DataCamp, edX, LinkedIn Learning and Udacity.)
Learn relevant programming skills
Obvious advice, perhaps, but you’ll want to do this before you start applying for data scientist jobs. Becoming proficient with fundamental languages like Python and SQL will likely be essential. But also take a look at data scientist job descriptions from the organizations you’d like to target for employment. What other types of languages do they expect for entry-level roles? That will give you a better sense of where to focus your learning.
Get to know the data science community
Look for opportunities online to network with data science professionals, or professionals aspiring to become a data scientist. You might check LinkedIn groups for data science professionals or consider reading data science blogs and following influential data scientists.
When you’ve connected with some established data scientists, consider asking them for informational interviews to learn more about their careers. Also, don’t overlook the peers, mentors and professional contacts already in your professional network. They might have suggestions for how to break into the data science field — and can possibly put you in touch with relevant contacts they know.
Start your own data science projects
Taking the initiative to build your own data science projects demonstrates a passion for learning, a quality that can give you an edge in the hiring process. It indicates to employers that you are committed not only to learning new skills, but also to applying them in creative and innovative ways just because you love it. A quick search online can help you find a wealth of ideas for data science projects for beginners.
Everything discussed here about how to become a data scientist in Canada can put you on track to building your career. Think about contacting specialized tech recruiters for assistance, as well. They can connect you with local organizations and employers who may be hiring for entry-level roles and also provide valuable job search tips.