With 2022 soon coming to an end, now is a great time to see what’s going on in data science when it comes to salaries. Here, you’ll see a list of some of the most common titles in the data science field, what each role makes, and some of the skills you’ll need just in case you might think about leaping elsewhere. Let’s get the show on the road and see how much data scientists make in the field!
Like many titles, “Data Scientist” can seem like a blanket term. Depending on the company and organization, what you do as a data scientist can vary quite considerably. But thanks to the U.S. Department of Labor Statistics, we have a good idea about what these folks make. As of May 2021, data scientists make an average of a comfortable 108,660 USD, according to their report on Occupational Employment and Wages. The same report also paints a clear picture of where you’re likely to find data science jobs with California, New York, and Texas being the top three places in the United States.
Data scientists are normally more experienced than their counterparts in data analytics and then have more managerial aspects of their roles, including skills focused on pure data science, such as SQL, R, Statistics, and Python.
Data engineers are the ones in the backend making the systems that store, process, and extract the data. They are experts at building and maintaining databases and the infrastructure that allows for databases to work seamlessly. They are also leaders who help to choose the applications and programs best suited for the organization while ensuring that data performance is optimized. Glassdoor currently has the aver salary of a data engineer at 152,569 USD.
Certifications are very important to data engineers. They not only demonstrate specific skills, but they also show potential employers they are knowledgeable about their systems. Normally, you’ll see data engineers with certifications from Amazon Web Services, Oracle, IBM, and others. So data engineers need to stay current with not only certification but skills that are specific to the systems where they operate.
Similar to data scientists, those who are data analysts tend to be seen as more in the trenches and a bridge between the data and the stakeholder. According to Glassdoor.com, data analysts on average can make a base salary of 67,260 USD. Unlike other roles that focus more heavily on data science, such as machine learning engineer, a data analyst must be skilled enough to clean, pull, interrupt, and finally present the data. Though they don’t dive into the data as deeply as business analysts, or data visualization experts, data analysts sit at the crossroad of big data ready to assist non-experts to understand the data presented.
Data analysts have normally seen as an entry-level point for those who want to enter the field of data science. So tools such as Excel/Google Sheets, basic SQL, and intermediate Python are often sought-after skills by employers who are wishing to hire a data analyst, while a college degree isn’t as important as in other fields.
Machine Learning Engineer
Artificial intelligence programs require those responsible for designing, building, and researching these programs. This is where machine learning engineers come into play. These specialists are problem solvers who are the intersection of software development/engineering, and analytics. They work tirelessly to maintain machine learning algorithms while ensuring data is kept clear and quality high. According to Salary.com, the median salary for machine learning engineers sits at 120,147 USD.
To become a machine learning engineer, a strong computer science and math background is a must. It’s critical to know applied mathematics and clear programming fundamentals. You’ll also be deep in data modeling, while also keeping your communication skills on par with your team and stakeholders.
Data Visualization Specialist
Data is useless unless it can be understood. That is where data visualization specialists come to play. These data professionals are the ones who create, edit and develop content based on the data they’re provided. Think of popular tools such as Tableau and others. These professionals often come from data analytics backgrounds and can utilize a variety of programs to create visual content for stakeholders based on the data presented.
According to Salary.com, the average salary for this title sits at 93,553 USD. Like a data analyst, a data visualization specialist must have both hard skills (R, Looker, Tableau, Microsoft BI, SQL, etc), and soft skills (presentation, interpersonal skills, etc).
No matter how clean or great your data is, if it’s not protected from bad actors, it could spell doom for your team and its goals. This is where cybersecurity specialists and professionals come in. These professionals are the ones who are taking measures to ensure your IT infrastructure is properly protected from both external bad actors, and internal negligence. They plan, coordinate, and implement security programs while keeping an eye on network devices for suspicious behavior.
According to Zippia.com, the median salary for a cyber security specialist sits at 93,395 USD. Basic networking and cybersecurity fundamentals are the best places to start if you’re interested in this field. Like data engineers, this position is dependent on system-specific certifications.
Though there are a lot of crossroads between data architects and engineers, as these two professionals are distinct and play different and important roles in the data team. These strategists are employing enterprise system databases so their organizations can get the most out of their systems while maintaining quality. They are also hands-on in the development, operations, programming, query processes, and security of native databases.
According to the U.S. Bureau of Labor Statistics, data architects command a mean annual salary of 121,840 USD. Like their machine learning partners, applied mathematics & statistics, SQL Server, and domain knowledge are important skills. They also need to have extensive knowledge of cloud computing design, architecture, and data lakes while maintaining Agile methodologies and resource planning.
Like their cousins in data analytics, business analysts work closely with data to help drive data-driven decision-making. Where they differ is that business analysts are focused on addressing business needs and presenting solutions to stakeholders. Similar to data analysts, the skills needed to become a business analyst are Excel/Google Sheets, SQL, and Tableau/Microsoft BI/Looker, with higher-level business analysts also possessing coding skills in R and Python for heavy visualization and data cleaning.
For business analysts, soft skills are just as important as they will not only be presenting data but also providing solutions that are data-driven to their stakeholders. Currently, according to Glassdoor.com, the average salary sits at 75,996 USD base.
Become a data scientist or a related role with ODSC West 2022
ODSC West is around the corner, coming up from November 1st to 3rd in San Francisco or virtually. There, you can get the hands-on training that you’ll need to upskill to take your career where you want to go. If you’re comfortable with your skillset and just want to get more career advice, earning what data scientists make or more, then check out the AI+ Career Expo at ODSC West and check out career talks, see what companies are hiring, and get your resume reviewed by a hiring manager. You can also sign up for AI+ Careers and find jobs in data science and AI year-round.