The most difficult part of any job-hunting is applying for the right job which matches your skills and expertise and not starts applying just for the sake of it.
The same logic applies to job hunting for any Data Science role. One should know the skills and expertise related to Data Science that can be utilized to perform a particular task that would eventually benefit an organization in solving a problem.
Because of the many roles and the different names, applicants may get confused and not know which role matches their specific skillsets or what they want to work on.
Realizing the newness of the Data Science field, U have written this article to help the new aspirants who want to transition into the field to align their existing skills and experience with the relevant job titles and learn the new skills to be more in aligning with the job titles of your choice as well.
Please note that the skills associated with the job titles below may change in the future. Also, some roles may overlap and have more or fewer responsibilities based on the company hiring. However, this article will help you understand the top 10 Data Science roles within almost every organization.
- Data Scientist
This is the most obvious, yet critical role related to Data Science within any organization. This role is all about dealing with every step of Data related task i.e., collecting, analyzing, visualization, and presenting data.
A Data Scientist knows a bit of everything; every step of the project and that’s why it enables them to offer the best solution to the problem that the company faces at that exact moment. Moreover, they are related to applying different Machine Learning algorithms and approaches as well.
Often in companies, Data Scientist is the one who has years of experience and they lead a team for accomplishing their tasks in time and with efficient approaches.
- Data Analyst
This is where the confusion related to job titles begins. Data Scientists and Data Analysts are overlapped in a company as they perceive them as one position.
Generally, Data Analysts are the data professionals who are responsible for visualizing, transforming, and manipulating existing data for exploratory analysis only. Sometimes they are also responsible for web analytics and A/B testing analysis.
Since data analysts are in charge of visualizing data, business acumen (knowledge) about the organization is also expected from them. It makes it easier for them for knowing what and from where relevant data should be imported, wrangled, and visualized in a proper way for discovering patterns and insights.
- Data Engineer
Data Engineers are the ones who are responsible for designing, building, and maintaining data pipelines for the Data team.
They are also responsible for batch processing of collected data and match its format to the stored data. In short, they make sure that the data pipelines are up to the standards that the relevant data is ready to be processed and analyzed.
Finally, they need to keep the ecosystem and the pipeline optimized and efficient and ensure that the data is available for data scientists and analysts to use.
- Data Architect
Data Architect is the one whose responsibilities overlap with of Data Engineers. They both need to ensure that the data being imported for analysis is well-formatted and accessible to data analysts and data scientists.
Additionally, Data Architects are responsible for developing new database systems that match the requirements of a specific business model and job requirements.
- Data Storyteller
As Data Science is evolving day after day, so are the job titles associated with it. Among those newer titles, Data Storyteller is on top of the list due to its significance and creativity attached to it.
Data Storytelling is often confused with Data Visualization. Although both job titles share tasks attached to them, there is a distinct difference between them. Data storytelling is not just about visualizing the data and making reports and stats; rather, it is about finding the narrative that best describes the data and uses it to express it.
It lays right in the middle between pure, raw data and human communication. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.
- Machine Learning Specialist
A machine learning scientist researches new data manipulating approaches and design new algorithms to be used. They are often a part of the R&D department, and their work usually leads to research papers. Their work is closer to academia yet in an industry setting.
- Machine Learning Engineer
Machine Learning Engineers, or ML Engineers in short; are in demand today. These are the professionals who are very familiar with the Machine Learning algorithms like clustering, classification, KNN, and others.
For performing the job properly, Machine Learning Engineers should be proficient in statistics and programming languages like Python or R studio besides basic software engineering concepts.
In addition to designing and building machine learning systems, machine learning engineers need to run tests — such as A/B tests — and monitor the different systems’ performance and functionality.
- Business Intelligence Developer
Business Intelligence Developers (BI Developers) need to be very comfortable using new BI tools or designing custom ones that provide analytics and business insights to understand their systems better.
BI Developer’s work is mostly business-oriented, where they are there within the organization to help the businesses to overcome their problems via the use of data.
- Database Administrator
A database administrator will be in charge of monitoring the database, making sure it functions properly, keep track of the data follow, and create backups and recoveries.
They are also in charge of granting different permissions to different employees based on their job requirements and employment level.
- Other Technologically Specific Roles
Data science is still a developing field; as it grows, more specific technologies will emerge, such as AI or specific ML algorithms. When the field develops in that manner, new specialized job roles will be created—for example, AI specialists, Deep Learning specialists, NLP specialists, etc.
Conclusion
The one thing I love about Data Science is the never-ending evolution of it and also the inclusion of all Data Science aspirants from different walks of life in it as everyone possess a different set of skills and expertise that can be utilized efficiently in the Data Science methodologies.
As the field of data science grows, the demand for data scientists grows as well. Not just that, new job roles get created to meet the huge demand of the industry. The variety of data science-related roles often means that their respective responsibilities overlap a little — and sometimes a lot —causing confusion for applicants trying to get their dream job.
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