Data scientist. It’s one of those technology jobs that sounds super-technical, a bit mysterious and, well, hard to get. However, landing a data scientist role, particularly at the entry level, may not be as insurmountable as you think.
First, know that there is demand for data science talent. In fact, the 2021 Robert Half Technology Salary Guide includes the position among a short list of “critical technology roles.” The guide also notes that demand for data science skills is likely to remain high as businesses accelerate their digital transformation process in the post-COVID-19 recovery.
Expect to see competition for skilled data science professionals across all major business sectors — from technology and manufacturing to financial services and healthcare — not to mention organizations in academia, government and the nonprofit sector. That’s because organizations of all types need data scientists to help translate the “story” of their big data, to turn numbers into recommended strategies and actions.
Unlocking the value of big data for business decision making
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, and social media channels, Internet of Things (IoT) sensors, and more.
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.
As for technical skills, here’s a look at what employers are typically looking for:
Data scientist job description: diverse technical skills required
Data scientists need to bring a range of analytical and mathematical skills 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.
Proven 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 Hadoop, Microsoft SQL Server database skills and Oracle database skills, and Extract, Transform, Load (ETL) skills, 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 Ph.D. can provide candidates an edge in the hiring process, and it’s an absolute requirement for some roles. And while a Ph.D. or other 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
So, 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? According to the 2021 Robert Half Technology Salary Guide, the salary midpoint (or median national salary) for the data scientist role is $129,000.
What does the midpoint represent? The midpoint, or what the guide refers to as the 50th percentile, means that candidates paid at that level have average experience with the necessary skills to meet the job requirements. To see the average compensation levels at the lower and higher ends of the salary spectrum for the data scientist role, refer to the salary tables in our latest Salary Guide, which you can download for free.
Note: Salary rates also differ by location. To determine a data scientist salary for your local market, use Robert Half’s Salary Calculator.
Laying the groundwork for a data science career
If you’re a college student or recent graduate considering the data science career path, 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:
Read books about online data analysis, statistics and data coding.
Look for e-books, online courses and video tutorials that dig deeper into data science and related topics that interest you. (Just some examples of resources offering online learning options for data science include Coursera, DataCamp, edX, Lynda.com 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 at 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 prefer 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 those aspiring to become a data scientist. As a starting point, see this list of LinkedIn groups for data science professionals. You might want to consider reading data science blogs (here’s a list to peruse) and following influential data scientists (another list to check out).
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, which 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 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 can put you on track to building an entry-level data scientist resume that can grab a hiring manager’s attention. Think about contacting specialized recruiters for assistance, as well. They can introduce you to local organizations and employers that may be hiring for entry-level roles and also provide valuable data scientist resume tips.