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Data science is still the job of the year, especially with all the hype in generative AI. However, it’s common that the demand for data science jobs is way lower than that of applicants; significantly, many employers still prefer senior data scientists over juniors. That’s why many students learning data science find it hard to find a job.
However, it doesn’t mean what you learn will go to waste. There are still many alternative career paths for those who know data science. For both beginners and professionals, there are various jobs where you can implement your data science skill set.
So, what are these alternative career paths? Here are five different jobs you should consider.
The first alternative career you can branch off from data science is machine learning engineer. People sometimes mistake these two occupations for being the same, but they are different.
Machine Learning engineers focus more on the technical aspects of machine learning deployment into production, such as how the structure should be designed or how the production should be scaled. On the other hand, data scientists focus on extracting insight from the data and providing solutions to solve the business problem.
Both share the same foundation in data analysis and machine learning, but the differences separate these career paths. If you feel that a Machine Learning Engineer position is for you, you should focus on learning more about software engineering practice and MLOps to switch to these careers.
The article How to Become a Machine Learning Engineer by Nisha Arya could also help you kickstart that career path.
The next job is a Data Engineer. In the current data-driven era, Data Engineer has become an important position to provide stable data stream with high quality. In the company, a Data Engineer would support many Data Scientist jobs.
Data Engineer works are focused on the backend infrastructure to support any data tasks and maintain the architecture for data management and storage. Data Engineer also focus on building the data pipelines as per requirements, including collection, transformation, and delivery.
The Data Engineer and Data Scientist work with data, but the Data Engineer focuses more on the data infrastructure. This means you must be adept in additional skills, including SQL, database management, and big data technologies.
To learn more about the Data Engineer career, read the article Free Data Engineering Course for Beginners by Bala Priya C.
Business intelligence (BI) is an alternative career path for those who still love to gain insight from the data but are more interested in analyzing historical data to inform the business. It’s an important position for any business as a company needs to know its current situation from the data.
BI focuses more on descriptive analytics, where business leaders and stakeholders use data insight to develop actionable initiatives. The insights would be based on current and historical data in the form of KPI and business metrics so the business could make an informed decision. To facilitate the analysis, BI uses tools to create dashboards and reports for the business. This makes BI different from data scientists because the latter job focuses on providing future predictions using advanced statistical analysis.
Many BI positions require skills such as basic statistics, SQL, and Data Visualization tools such as Power BI. These are skills that people have to learn when they try to become data scientists, so BI would be a suitable alternative career path for those who love analyzing data.
If you want to improve your skills for a BI position, the article Big Data Analytics: Why Is It So Crucial For Business Intelligence? by Nahla Davies would give you that edge.
A Data Product Manager might be perfect if you want to move into a position with less technicality but still related to data science. This is a position that prefers skillset for a strategy to create a roadmap for the data-centric products or services
The Data Product Manager job focuses more on understanding the current market trends and guiding the data product development to meet the customer needs. The position should also understand how to position the product or services as a company asset. At the same time, the Data Product Manager should have the technical knowledge to communicate with the technical people and manage the strategy for product development.
Typically, a data product manager should have skills that include business understanding, data technology understanding, and customer experience design. These skills are necessary if the Data Product Manager wants to succeed in this position. You can read the article here to understand more about Data Product Manager.
The last career path you should consider is the Data Analyst. The data analysts usually work with the raw data to provide answers to specific questions that are required by the business. It contrasts with the works of BI as although they have overlapping skills, BI usually uses tools to create dashboards and reports to track the KPI and business metrics continuously. In contrast, data analysts typically work on a project basis.
Data analysts often work in each department to provide detailed ad-hoc analysis for the specific project and perform statistical analysis to gain insight from the data. Data analysts can use SQL, programming language (Python/R), and data visualization tools, which are skills that data science has learned.
If this is an alternative career path, you could attend a Free Data Analyst Bootcamp for Beginners, as explained by Bala Priya C.
If the data science path is not for you, there are still many alternative careers you could try on. You don’t need to waste the skill you have learned, so here are the top five data science alternative career paths you should consider:
- Machine Learning Engineer
- Data Engineer
- Business Intelligence
- Data Product Manager
- Data Analyst
I hope it helps! Share your thoughts on the communities listed here, and add your comment below.
Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and Data tips via social media and writing media.
