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Data science continues to be the job of the year, especially with all the hype around generative ai. However, it is common for the demand for data science jobs to be much lower than the applicants; Significantly, many employers still prefer senior data scientists over junior ones. This is why many students learning data science find it difficult to find jobs.
However, that doesn't mean that what you learn goes to waste. There are still many alternative career paths for those who know data science. For beginners and professionals alike, there are several jobs where they can implement their data science skills.
So what are these alternative career paths? Here are five different jobs you should consider.
The first alternative career that can come from data science is that of a machine learning engineer. Sometimes people confuse these two occupations with the same thing, but they are different.
Machine Learning engineers focus more on the technical aspects of deploying machine learning in production, such as how the framework should be designed or how production should be scaled. On the other hand, data scientists focus on extracting insights from data and providing solutions to solve the business problem.
They both share the same foundation in data analytics and machine learning, but differences separate these career paths. If you think a machine learning engineer position is for you, you should focus on learning more about the practice of software engineering and MLOps to transition into these careers.
The article How to Become a Machine Learning Engineer by Nisha Arya could also help you get started on that career path.
The next job is data engineer. In today's data-driven era, data engineer has become an important position to provide stable data flow with high quality. In the enterprise, a data engineer would support many data scientist jobs.
Data engineer jobs focus on backend infrastructure to support any data tasks and maintain the architecture for data management and storage. The data engineer also focuses on building 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 data infrastructure. This means he must have additional skills, including SQL, database administration, and big data technologies.
To learn more about a career as a data engineer, 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 deriving insights from data but are more interested in analyzing historical data to inform the business. It is an important position for any business, since a company needs to know its current situation from data.
BI focuses more on descriptive analytics, where business leaders and stakeholders use insights from data to develop actionable initiatives. The insights would be based on current and historical data in the form of KPIs and business metrics so that the company can make an informed decision. To facilitate analysis, BI uses tools to create dashboards and reports for the business. This differentiates BI from data scientists because the latter work focuses on providing future predictions through advanced statistical analysis.
Many BI positions require skills such as basic statistics, SQL, and data visualization tools like Power BI. These are skills that people need to learn when trying to become data scientists, so BI would be a suitable alternative career path for those who love data analysis.
If you want to upskill yourself for a BI role, check out the article Big Data Analytics: Why is it so crucial for business intelligence? by Nahla Davies would give you that advantage.
A data product manager may be perfect if you want to move into a role with less technicalities but still related to data science. This is a position that prefers a strategy skill set to create a roadmap for data-centric products or services.
The Data Product Manager's job focuses more on understanding current market trends and guiding the development of data products to meet customer needs. The role should also understand how to position the product or service as an asset of the company. At the same time, the Data Product Manager must have the technical knowledge to communicate with technical staff and manage the strategy for product development.
Typically, a data product manager should have skills that include business understanding, understanding of data technology, and customer experience design. These skills are necessary if the Data Product Manager wants to be successful in this position. You can read the article. here to understand more about Data Product Manager.
The last career path you should consider is Data Analyst. Data analysts typically work with raw data to provide answers to specific questions that the business requires. It contrasts with BI jobs because, although they have overlapping skills, BI typically uses tools to create dashboards and reports to continuously track KPIs and business metrics. In contrast, data analysts usually work on a project basis.
Data analysts typically work in each department to provide detailed ad hoc analysis for the specific project and perform statistical analysis to derive insights 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 can attend a free Data Analyst Bootcamp for beginners, as explained by Bala Priya C.
If the data science path isn't for you, there are still many alternative careers you can try. You don't need to waste the skill you've learned, so here are the top five alternative career paths in data science you should consider:
- Machine learning engineer
- data engineer
- Business Intelligence
- Data Product Manager
- Data analyst
I hope that helps! Share your thoughts on the communities listed here and add your comment below.
Cornellius Yudha Wijaya He is an assistant data science manager and data writer. While working full-time at Allianz Indonesia, she loves sharing Python tips and data through social media and print media.
