By comparison, the data science market is growing far faster than most. An estimated 90% of the world’s data was created over just the past two years, creating an ever-growing demand for data interpretation and analysis.
This gap between data experts and available job openings has left many organizations struggling to fill six-figure data assessment positions.
Still the number one job position on Glassdoor, data science is a need estimated to grow even larger over the course of the next decade. According to the U.S. Bureau of Labor Statistics, roughly 11.5 additional data science positions will be created by 2026.
If you’re interested in pursuing a position that helps you address the exponentially-growing need for data science professionals, you should know that data science careers don’t take one shape or size. Depending on your organization’s needs, you might wear several hats in the data science field.
Which data science career is right for me?
Your skillset and career outlook should shape your future in the data science profession. Whether you’re interested in programming, reporting, artificial intelligence or statistical analysis, there’s a data science career ideal to suit your interests.
Data analyst
Despite heavy advances in artificial intelligence, machine learning and automation, data sets still require analysis. Depending on where your company sources its data — website traffic, online user activity, call logs, purchase registries, etc. — data analysts are asked to translate all information into useful conclusions.
Data analysis isn’t always easy. Often, data analysts face the uphill task of sorting large volumes of data. Data isn’t often presented in a user-friendly format; that’s why it’s important for data analysts to use tools that can help them soft through metrics quickly.
As a data analyst, you’ll analyze data in a variety of ways, likely applying both AI and statistical algorithms that take some of the heavy lifting out of the process. You’ll also need to understand how, and where, to find data for extraction and analysis.
Average Salary: $80,100 per year
Machine learning engineer
Artificial intelligence can make tasks easier across an organization — not just for individuals fulfilling a data science career. Machine learning engineers work hard to create, implement and optimize automated programs that make mundane, complicated or excessively long processes easier to handle.
Education is a major component of your role as a machine learning engineer. You’ll need to ensure that you remain up to date on the latest trends and machine learning practices. When a new application for machine learning arises, you’ll be ready to handle it.
Machine learning engineers also have a role in verifying the quality of data that a data scientist or researcher or analyst might mine. You’ll select the data presentation model that best accommodates the data set, and play a heavy role in implementing any of that data in new, or existing, machine learning processes.
Average Salary: $131,000 per year
Data scientist
Every single day, organizations generate thousands of new data points. No matter the size of your company, you might collect information across a variety of different touchpoints — including your website, social media pages, call or customer support center, physical storefront or other customer correspondence.
It’s important that each organization understands exactly how data is collected and used, so that data can contribute to overall company growth.
Data scientists play a large role in correctly interpreting data on a company’s behalf. For example, a data scientist might analyze the transaction history for a particular product or a particular data range, to isolate user history and identify products to potentially bundle for larger profits.
As a data scientist, your responsibilities focus on the collection and analysis of a company’s data. You’ll often handle data from the aggregation stage through reporting, helping stakeholders understand the significance of data trends you find.
Average Salary: $136,300 per year
Big data architect
Sometimes, data sets can grow quite large. For example, the complete transaction history for a large company over the past 18 months is quite a large file. When it comes to analyzing extensive data sets — or implementing algorithms that make analysis easier for these larger data sets — companies often call on the help of a big data architect.
Big data architects work closely with an organization’s IT department, to coordinate data sources in ways that make management easier. They work to ensure that data remains organized. More importantly, they work to make sure that systems and hierarchies remain in place that make ongoing data analysis easier and more fruitful.
As a dig data architect, you’ll likely work alongside other members of a data science team — since big data architects are most commonly employed at larger organizations. Big data architects help all data science team members access information, and the systems that store the raw data sets.
Average Salary: $162,200 per year
Statistician
Often found in fields like education and athletics, statisticians play an important role in fulfilling critical data science responsibilities. Statisticians help data scientists, analysts, engineers and other team members collect and analyze the information they find.
Statisticians might also help data science teams “cleanse” data sets, removing irrelevant data and organizing the most relevant data to make conclusions easier to reach.
Once a statistician, or other member of the data science team, is able to reach meaningful conclusions from collected data, those conclusions are typically communicated to stakeholders. Statisticians can play a public-facing role as well, packaging relevant conclusions from analyzed data and presenting those findings directly to executives.
Average Salary: $133,300 per year
Data science manager
Typically found in a leadership role in larger organizations, a data science manager helps facilitate smooth data science team management. Often, this means fulfilling both an administrative and an analysis role: many data science managers ensure that teams operate well, while also performing a portion of data assessments themselves.
You’ll need a deep leadership and analysis skillset to thrive in a data science manager position. While you mine, analyze and package data yourself, you’ll also have to make sure that other members of your data science team are performing their own roles well. Often, a data science manager is the first point of contact for a corporate executive, whenever they’re looking to mine new conclusions from the data sets they collect over time.
Average Salary: $120,000 per year
The data science market is a fast-moving world — where new programming methods, artificial intelligence systems and best practices always require new education. In fact, continuing education is the number one Datamation data science trend to look for in 2022.
Virtually every data science career needs an education partner that’s dedicated to your success, one that’s willing to work as hard as you do to ensure that you learn and retain the skills you’ll need to thrive on a daily basis.
QuickStart is that partner. Our fully online Data Science & Analytics Bootcamp provides you all the skills you need to improve your earning power, enhance credibility and deepen fundamental data science abilities.
Learn the skills that today’s employers look for, all in 28 weeks or less.
Earn the data science job you deserve, or take steps to improve your current data science career positioning, when you contact our on-demand Admissions team today.