12 Data Science Job Titles — Which Role Is Right for You?

When I first started as a data scientist, I was baffled by the different types of data science positions and their responsibilities. I didn’t want to apply for a job when it wasn’t even clear what I would be doing. Because of all the data science roles out there — and their nuanced job descriptions — you may also be confused. Which role matches your specific skill set? How do you know what you’ll be working on?

Let’s look at the differences between some of the most popular data science roles and what they actually do. 

1. Data Scientist

As a data scientist, you’ll deal with all aspects of a project from knowing what’s important to the business, to data collection and analysis and finally to data visualization and presentations.

A data scientist is a jack of all trades. As a result, they can offer insights into the best solutions for a specific project while uncovering larger patterns and trends in the data. Moreover, companies often charge data scientists with researching and developing new algorithms and approaches.

In large companies, team leads are often data scientists because their skill set allows them to oversee other employees with specialized skills while guiding a project from start to finish.

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2. Data Analyst

In your job search, you may also come across the role of data analyst. Data science and data analysis sometimes overlap. In fact, a company may hire you as a “data scientist” when most of the job you’re actually doing is data analytics.

Data analysts are responsible for different tasks such as visualizing, transforming and manipulating data. Sometimes they’re also responsible for web analytics tracking and A/B testing analysis.

Since data analysts are in charge of visualization, they’re often responsible for preparing the data for business communications. Analysts prepare reports that effectively show the trends and insights they gather from their analysis in a way that non-specialists can understand.

3. Data Engineer

Data engineers are responsible for designing, building and maintaining data pipelines. They test ecosystems for businesses and prepare them for data scientists to run their algorithms. Data engineers also work on batch processing of collected data and match its format to the stored data. Finally, engineers keep the ecosystem and the pipeline optimized and efficient to ensure data is available for data scientists and analysts to use at any moment.

4. Data Architect

Data architects share common responsibilities with data engineers. They both need to ensure the data is well-formatted and accessible for data scientists and analysts and improve the data pipelines’ performance.

In addition, data architects design and create new database systems that match the requirements of a specific business model. Architects need to maintain these database systems, both functionally and administratively. In other words, architects keep track of the data and decide who can view, use and manipulate different sections of the data.

5. Data Storyteller

Often, data storytelling is confused with data visualization. Data storytelling is not just about visualizing the data and making reports to share stats; it’s about finding the narrative that best describes the data and developing creative ways to express that narrative.

Data storytelling straddles the line between pure, raw data analysis and human-centered communication. A data storyteller needs to take data, simplify it to focus on a specific aspect of the data, analyze its behavior and then use their own insights to create a compelling story that helps different audiences better understand a given phenomenon. This position offers significant value to a team while creating an opportunity for data scientists to flex their creative muscles. 

6. Machine Learning Scientist

A machine learning scientist researches new approaches to data manipulation to design new algorithms. They’re often part of the R&D (research and development) department and their work usually leads to published research papers. Machine learning scientists typically work in academia rather than industry. You may also see machine learning scientists referred to as research scientists or research engineers.

7. Machine Learning Engineer

Machine learning engineers are in high demand. They need to be familiar with the various machine learning algorithms like clustering, categorization and classification while staying up-to-date with the latest research advances in the field.

Machine learning engineers need to have strong statistics and programming skills, along with fundamental knowledge of software engineering. In addition to designing and building machine learning systems, machine learning engineers need to run tests while monitoring the different systems’ performance and functionality.

8. Business Intelligence Developer

Business intelligence (BI) developers design strategies that allow businesses to find the information they need to make decisions quickly and efficiently. To do that, BI developers need to be comfortable using new BI tools or designing custom ones that provide analytics and business insights.

A BI developer’s work is mostly business-oriented so they need to have at least a basic understanding of the fundamentals of business strategy, as well as the ins and outs of their company’s business model. 

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9. Database Administrator

Many companies design a database system based on specific business requirements but the company buying the product will actually manage the system. In such cases, a company will hire a person (or a team) to manage the database. A database administrator will monitor the database to make sure it functions properly and keep track of the data flow while creating backups and recoveries. Administrators also oversee security by granting different permissions to employees based on their job requirements and employment level.

10. Statistician

While statisticians and data scientists have overlapping responsibilities, there are key differences in how they fulfill their roles. Data scientists work with a broader range of disciplines like machine learning, software engineering and automation. On the other hand, statisticians are more focused on using statistical models and mathematical concepts to discern quantitative relationships in data and solve problems. 

11. Data Privacy Officer

The growing number of data privacy laws has made data privacy officer (DPO) an essential role for many companies. To ensure businesses remain in compliance with regulations, DPOs collaborate with departments and leadership to design data protection strategies, develop best practices for defending personal information and assess a company’s digital assets to resolve any data-related privacy risks. 

12. AI Ethics Officer

AI ethics officers develop guidelines and values that an organization can follow to design and deploy AI in a safe and legal manner. They translate these values into concrete actions by writing company policies and making sure all personnel comply with these rules. 

Working with data engineers, data scientists, machine learning engineers and other team members, these officers can enforce using accurate data to avoid algorithmic bias, receiving consumer permission before accessing personal data and other best practices for handling data.

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