Top 5 Alternative Career Paths in Data Science

Top 5 Alternative Career Paths in Data ScienceTop 5 Alternative Career Paths in Data Science
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Data science is still the job of the year, especially with all the hype surrounding generative artificial intelligence. However, the demand for data science jobs is typically much lower than the demand for candidates; significantly, many employers still prefer older data scientists over younger ones. That's why many students studying data science find it difficult to find a job.

However, this does not mean that what you have learned will fail. 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 apply 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 spin off from data science is a machine learning engineer. People sometimes mistakenly think that these two professions are the same, but they are different.

Machine learning engineers focus more on the technical aspects of implementing machine learning in production, such as how a structure 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 business problems.

Both share the same foundations in data analysis 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 software engineering practices and MLOs to transition into those careers.

Nisha Arya's How to Become a Machine Learning Engineer article might also help you launch that career.

The next job is Data Engineer. In the current data-driven era, a Data Engineer has become an important position to provide a stable flow of high-quality data. In a company, a Data Engineer would support many jobs of a Data Scientist.

A Data Engineer's work focuses on the back-end infrastructure to support all data tasks and maintain the architecture for data management and storage. The Data Engineer also focuses on building the data pipeline to requirements, including collection, transformation and delivery.

Data Engineer and Data Scientist work with data, but Data Engineer focuses more on data infrastructure. This means you need to be proficient in additional skills, including SQL, database management, and big data technologies.

To learn more about a career as a Data Engineer, read Bala Priya C's article Free Data Engineering Course for Beginners.

Business Intelligence (BI) is an alternative career path for those who still like to gain insight from data, but are more interested in analyzing historical data to inform business. It is an important position for any business because the company needs to know its current situation from the data.

BI focuses more on descriptive analytics, where business leaders and stakeholders use data insights to develop actionable initiatives. 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 business dashboards and reports. This makes BI different from data scientists as 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 the skills that people need to learn when they want to become data scientists, so BI would be a suitable alternative career path for those who like to analyze data.

If you want to improve your skills for a BI position, the article Big Data Analytics: Why Is It Crucial For Business Intelligence? by Nahla Davies will give you an edge.

A data product manager could be perfect if you want to transition to a position with less technical questions but still related to data science. This is a position that prefers the skill set to create a plan strategy for data-centric products or services

The job of a data product manager focuses more on understanding current market trends and leading the development of data products to meet customer needs. The position also needs to understand how to position the product or services as an asset of the company. At the same time, the Data Product Manager should have the technical knowledge to communicate with 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 user experience design. These skills are essential if the Data Product Manager is to succeed in this position. You can read the article here to learn more about Data Product Manager.

The last career you should consider is a data analyst. Data analysts typically work with raw data to provide answers to specific business-required questions. This contrasts with BI works because, although they have overlapping skills, BI typically uses tools to create dashboards and reports to continuously monitor KPIs and business metrics. In contrast, data analysts typically work on a project basis.

Data analysts often work in each department to provide detailed ad-hoc analysis for a specific project and perform statistical analysis to gain insight into the data. Data analysts can use SQL, a programming language (Python/R), and data visualization tools, which are skills taught in data science.

If this is an alternative career path, you can attend a free data analyst boot camp, as explained by Bala Priya C.

If the data science path isn't for you, there are still many alternative careers you could try. You don't need to waste the skill you've learned, so here are the top five alternative data science careers you should consider:

  1. Machine learning engineer
  2. Data engineer
  3. Business intelligence
  4. Head of Data Products
  5. Data analyst

I hope it helps! Share your thoughts on the communities listed here and add your comment below.

Cornellius Yudha Wijaya is an assistant head of data science and a data writer. While working full-time at Allianz Indonesia, he likes to share Python and data tips through social media and writing media.

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