Data scientist. It’s one of those technology jobs that sounds super-technical, a bit mysterious and, well, hard to get.
Surprise. The hurdles to becoming a data scientist, particularly at the entry level, may not be as insurmountable as you think.
Data science is a rapidly growing field, and skilled and experienced data scientists available for hire are scarce. That means competition is intense across all major business sectors — from technology and manufacturing to financial services and healthcare — not to mention all the organizations in academia, government and the nonprofit sector. It seems everyone is eager to recruit this type of quantitative talent to support their data-driven research and other initiatives.
This supply-and-demand challenge is reflected in compensation trends for data scientists. According to the 2019 Robert Half Technology Salary Guide, the salary midpoint for a data scientist is $121,500.
Data scientists help to translate big data’s ‘story’
So why exactly are data scientists in such high demand? As the world becomes increasingly data-driven, data gets more and more valuable — as long as you have a way to put it to practical business use. Often referred to as “actionable insights,” this business intelligence is used to inform decisions about everything from new product development to marketing campaigns to supply chain design. Companies are also relying more on these insights to help them improve cybersecurity, employee retention, recruitment and productivity, customer service and engagement, and much more.
This is where the data scientist comes in. Businesses need people with knowledge of statistics and modeling to unlock the value of complex, unprocessed data from an array of sources. Those sources can include everything from machine log data, digital media and documents, databases, the web, and social media channels.
Sensors are another source, and they’re multiplying fast as the Internet of Things (IoT) takes shape. They are being built into everything from thermostats to cars to medical devices, and organizations need to analyze the data these sensors generate.
Because data-driven business intelligence can be used in so many ways in an organization, companies want to hire data scientists who also have a head for business. Communication abilities and other soft skills are also paramount for today’s data scientist jobs. That’s because these professionals must be able to explain quickly to nontechnical people the “So what?” factor — the story the data is telling about 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.
Now, let's look at types of skills you’ll likely need on the technical side for this job.
Data scientist job description: diverse technical skills required
To derive insights that can benefit businesses, data scientists also need to bring a range of mathematical and analytical skills to their roles. Data science is essentially a blend of statistics and mathematics and computer science. So, many employers look specifically for candidates who have expertise in statistics or similar areas like machine learning that can help data scientists to identify patterns in data.
Proven experience working with programming languages, such as Python or Java, is also often part of the data scientist job description. However, many businesses want to hire professionals who can work with other languages, tools and applications like R, a language used for statistical analysis, data visualization and predictive modeling, and Tableau, a tool for interactive data visualization.
Robert Half Technology’s latest Salary Guide notes that data scientists can earn between 6 and 8 percent more in starting compensation if they have in-demand skills like Hadoop, Microsoft SQL Server database skills and Oracle database skills. Extract, Transform, Load (ETL) skills, like database schema design and systems building, are also highly valued and can increase a data scientist salary (base pay) by as much as 6 percent, the guide reports.
Some employers are willing to flex on certain technical requirements in their data scientist job descriptions, however. Because highly skilled data scientists are so hard to find in the current hiring market, more businesses are now willing to train promising candidates for entry-level positions in this area.
Education key for advanced roles
Many organizations prefer to hire data scientists who have earned a Ph.D. in a relevant subject-matter area like mathematics or computer science. A Ph.D. undoubtedly can provide candidates an edge in the hiring process — and is an absolute requirement for some roles. This or other advanced degrees may not be essential for an entry-level data scientist role, however. But keep in mind that having a Ph.D. may become more important as you look to advance in your data science career.
Laying the groundwork
If you’re a college student or recent graduate considering this challenging career path, job requirements that are non-negotiable 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:
- Read books about online data analysis, statistics, and data coding. Yes, books — really! (See this Hackernoon blog post for some reading list recommendations from data analyst and researcher Tomi Mester.) Also, look for online courses and video tutorials that dig deeper on data science must-haves and related topics that interest you. (Some examples of resources include Cloudmentor and Lynda.com.)
- 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 companies 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.
- Start your own 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 ideas for data science projects for beginners — for example, this article from Data Science Weekly recommends making a data visualization.
Also, be sure to tap into your network for input. Your peers, mentors and professional contacts might know of other skills that can help you break into the data science field — or at least put you in touch with other people who may know. Another strategy: Use professional networking sites to connect with people who work in the industry and ask for informational interviews to learn more about their careers.
All these activities 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 can also provide valuable data scientist resume tips.
Looking for data scientist jobs?
Search Robert Half Technology’s job site to find data scientist positions and other roles for data professionals that may be available in your area.
This post has been updated to reflect more current information.