10 Data Science Job Titles: Types of Data Science Jobs – Built In

Job hunting is a hassle. It’s a brutal game. You need to stand out among hundreds (if not thousands) of other applicants to get the job but even finding the right role to apply for in the first place isn’t easy.
When I first started as a data scientist, I was baffled by the different types of data science jobs 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?
So let’s look at the differences between certain data science roles and what they actually do. 
Keep in mind  these titles aren’t fixed and may change in the future. Also, some roles may overlap and have more or fewer responsibilities based on the company to which you’re applying. However, this list of the top 10 data science job titles will help you get started as you explore job postings. 
 
Let’s start with the most general role: 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 on 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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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 the 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.
 
Data engineers are responsible for designing, building and maintaining data pipelines. They need to 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 need to keep the ecosystem and the pipeline optimized and efficient to ensure the data is available for data scientists and analysts to use at any moment.
 
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.
 
Often, data storytelling is confused with data visualization. Although they do share some commonalities, there’s a distinct difference between them. 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 people (fellow teammates, customers, etc.) better understand a given phenomenon. This is probably the newest job role on this list—one that has the potential to offer significant value to a team while also creating an opportunity for data scientists to flex their creative muscles. 
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Most often, when you see the term “scientist” in a job role, it indicates this job role requires doing research to develop new algorithms and insights. In this case, 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.
 
 
Machine learning engineers are in high demand today. 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.
For machine learning engineers to perform their job properly they need to have strong statistics and programming skills in addition to some fundamental knowledge of software engineering. In addition to designing and building machine learning systems, machine learning engineers need to run tests (such as A/B tests) while monitoring the different systems’ performance and functionality.
 
Business intelligence developers — also called BI developers — take charge of designing 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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Sometimes the team designing the database is not the team using it. Currently, 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.
 
Data science is a constantly developing field; as it grows, more specific technologies will emerge, such as AI or specific ML algorithms. Consequently, when the field expands, new specialized job roles will likewise emerge. For example, AI specialists, deep learning specialists, NLP (natural language processing) specialists, etc.
This expanding specialist field applies to data scientists and analysts as well. For example, transportation DS specialist, marketing storyteller, and so on. These roles will be particular to the responsibilities of the business and likely lighten the workload for generalist scientists and engineers. 
As the field of data science evolves, the demand for data scientists grows and businesses create new jobs every day to meet the industry’s huge demands. The variety of data science jobs often mean responsibilities overlap a little (sometimes a lot) which can confuse applicants trying to land their dream job. Hopefully, now you have a somewhat clearer understanding of the best jobs for your skill set.
Good luck!
Built In’s expert contributor network publishes thoughtful, solutions-oriented stories written by innovative tech professionals. It is the tech industry’s definitive destination for sharing compelling, first-person accounts of problem-solving on the road to innovation.

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