5 min read
Job hunting is a hassle. It’s a brutal game. You need to stand out among hundreds of other applicants to get the job but even finding the right role to apply for in the first place isn’t easy.
Because of all the data science roles out there—and their nuanced job descriptions—you may also get 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.
5 DATA SCIENCE JOB TITLES.
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineer
- Database Administrator
1. Data Scientist
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.
Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies make better decisions.
LeCun is one of the most well-known data scientists of our time. He is well known as the Director of AI Research at Facebook but has made industry-changing inventions that earned him a spot at the top of the list of best data scientists in the world.
2. Data Analyst
Data scientists and data analysts sometimes overlap. In fact, a company may hire you as a “data scientist” when most of the job you will actually be doing is data analytics.
Data analysts are responsible for a variety of tasks including visualization, cleaning, and processing of huge amounts of data. They also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.
Alex “Sandy” Pentland is termed as one of the world’s seven most powerful data scientists along with Larry Page, by Tim O’Reilly in 2011. Mr. Pentland also founded and leads an MIT-wide program that works actively in pioneering computational social science using Big Data and AI.
3. Data Engineer
Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized.
Data engineers are responsible for designing, building and maintaining data pipelines. They need to test ecosystems for businesses and Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.
Data scientists and data analysts analyze data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing that high-quality data. Data engineers gather and prepare the data and data scientists use the data to promote better business decisions.
4. Machine Learning Engineer
Machine Learning: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data:
Machine learning is a part of the data science field specifically concerned with artificial intelligence. It uses algorithms to interpret data in a way that replicates how humans learn. The goal is for the machine to improve its learning accuracy and provide data based on that learning to the user.
Often, a machine learning engineer will also serve as a critical communicator between other data science team members, working directly with the data scientists who develop the models for building AI systems and the people who construct and run them.
Andrew Ng has been the chief scientist at Baidu Research, founder of deeplearning.ai, adjunct professor at Stanford University, and founder and chairman of the board at Coursera. He founded the Google Brain project, which is behind the development of large-scale artificial neural networks, including one that taught itself to recognize cats in videos.
5. Database Administrator
A database administrator(DBA) is responsible for maintaining, securing, and operating databases and also ensures that data is correctly stored and retrieved.
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. The specific responsibilities of a database administrator vary depending on the size and needs of the organization they work for. However, most DBA duties will include developing and maintaining databases, ensuring data security, tuning performance, backing up data, and providing training and support to users.