Hunting for a job can be a hassle. Some call it a brutal game where hundreds and thousands of applicants fight for a handful of positions. Even finding the right job to apply for can be a troubling task, especially if you want to work in a field like Data Science that is full of different types of job titles. Because of the different names and roles, applicants might get confused about what role matches their skill sets. That is why, in this article, we have listed the different data science titles in the market so that you know which one is the right option for you:
1. Data scientist:
This is the most general and popular role in the field. If you work as a data scientist, you will be required to work on all the aspects of the project including data collection, analysis, visualization, presentation, and even the business side. You must know something about everything so that you can offer the best solutions at every step of the project and uncover trends and patterns. Also, they lead the research and development process of new approaches and algorithms. In big companies, team leaders are often the ones with specialized skill sets like a data scientist as their skills allow them to guide and overlook the project from start to finish. The average salary of a data scientist in India is around Rs. 10,00,000 per annum.
2. Data Analyst:
The second most popular job title in the field of data science is a data analyst. Data analysis and data science go hand in hand and sometimes, a company might overlap and hire you to do the work of a data analyst while giving you the title of a data scientist. As a data analyst, you will be responsible for a multitude of tasks including manipulating, transforming, and visualizing the data. In some cases, you will also have to perform A/B testing analysis and web analytics tracking. Since you are also in charge of data visualization, you will have to prepare the data in a way that can be communicated to the business side of the project. This involves creating reports that show the insights and trends gathered from the analysis effectively.
3. Data Engineer:
The job of a data engineer involves designing, developing, and maintaining the data pipelines. For this, you will test the ecosystems and prepare them to be used by data scientists for running their algorithms. You will also be working on batch processing and matching the format of the collected data to the stored data. You will have to ensure that the data is ready for analysis and processing. Finally, you have to optimize the pipeline and the ecosystem while making sure that the data is available for data analysts and data scientists to use.
4. Data Architect:
If you are working as a data architect, you will be sharing some responsibilities with data engineers. It will be your job to improve the performance of data pipelines and ensure that the data is properly formatted and accessible for data analysts and data scientists. Apart from this, you will also have to design and create database systems matching the requirements of your job requirements and the company’s business model. These database systems have to be maintained as well, both from the administrative perspective and the functionality one. It will be your responsibility to track the data and decide who can view, manipulate, and use the different sections of data.
5. Data Storyteller:
This is a comparatively new job role, but it is creative and significant. Some people confuse data storytelling with data visualization. And even though there are some commonalities, both of them have a distinct difference. Data storytelling is more than just making stats and reports and visualizing the data; rather, it is about describing data using the best narrative. It is somewhere between human communication and raw data. As a data storyteller, you will have to simplify the data, analyze its behavior, and create a compelling story using the insights so that people can better understand the data.
6. Machine Learning Scientist:
When you see “scientist” in the job title, it is an indication that this job will require research and coming up with new insights and algorithms. As a machine learning scientist, you will have to research new approaches to data manipulation and create new algorithms. You will be a part of the R&D team and work on research papers. Your job will be closer to academia but in an industry setting. Some other titles used for describing machine learning scientists are research engineers and research scientists.
7. Machine Learning Engineer:
If you are working as a machine learning engineer, you will be in-demand. Your job will include working with different machine learning algorithms like classification, categorization, and clustering and staying updated with the latest advances in the field. You must have strong programming and statistics skills apart from some fundamentals of software engineering. You will be responsible for designing and developing machine learning systems, running tests like A/B, and monitoring the functionality and performance of the systems.
8. Business Intelligence Developer:
Also called BI developer, this job role involves leading the strategies design and development process so that the business users can find the information needed for making quick and efficient decisions. To work in this role, you should know how to work with BI tools or design custom ones that can offer business and analytics insights that help in understanding the data better. Your work will be business-oriented; that’s why you need a basic understanding of the business models and their implementation.
If you want to work in any of these roles, you can enroll yourself in Simplilearn’s Data Science program. The course will prepare you for the role that you desire and help you get the desired skill set.
Lucia Patterson is the woman behind TheLegalGuides, a blog solely focused on legal guides, tips, and advice. Lucia loves essay writing and blogs at EssayWritingGuides from her college days. Online Marketing Tools, Smart Business Daily, Emblem Wealthare some of another sites, she is used to contribute.