Top 5 Alternative Career Paths for Data Scientists
Data science is still the job of the year, especially given the hype surrounding generative AI. However, it is common that the demand for data science jobs is much lower than the number of applicants; additionally, many employers continue to prefer senior data scientists over juniors. This is why many data science students struggle to find work.
However, this does not imply that what you learn will be wasted. There are still numerous career options for those who understand data science. There are numerous jobs available for both beginners and professionals to put their data science skills to use.
So, what are the alternative career paths? Here are five different jobs to consider.
1. Machine Learning Engineer
The first alternative career path from data science is machine learning engineer. People often confuse these two occupations, but they are not the same.
Machine Learning engineers are more concerned with the technical aspects of machine learning deployment in production, such as how the structure should be designed or how production should be scaled. On the other hand, data scientists are concerned with extracting insight from data and providing solutions to business problems.
Both have a foundation in data analysis and machine learning, but the differences distinguish these career paths. If you believe that a position as a Machine Learning Engineer is a good fit for you, you should learn more about software engineering practice and MLOps before switching careers.
2. Data Engineer
The next job is as a Data Engineer. In today’s data-driven world, Data Engineers play an important role in ensuring a consistent and high-quality data stream. A Data Engineer would support many Data Scientist positions within the company.
Data Engineers are responsible for the backend infrastructure that supports all data tasks and maintains the architecture for data management and storage. Data engineers are also responsible for building data pipelines based on requirements, such as collection, transformation, and delivery.
The Data Engineer and Data Scientist both work with data, but the Data Engineer focuses on the data infrastructure. This requires you to be proficient in additional skills such as SQL, database management, and big data technologies.
3. Business Intelligence Analyst
Business intelligence Analyst (BI) is an alternative career path for those who enjoy gaining insights from data but prefer to analyse historical data to inform business decisions. It is an important position for any business because it allows the company to determine its current situation based on data.
BI Analysts focus on descriptive analytics, in which business leaders and stakeholders use data insights to create actionable initiatives. The insights would be based on current and historical data in the form of KPIs and business metrics, allowing the business to make informed decisions. BI Analysts employ tools to generate business dashboards and reports to aid in analysis. This distinguishes business intelligence from data science, which focuses on making future predictions using advanced statistical analysis.
Many BI Analyst positions require basic statistics, SQL, and data visualization tools like Power BI. These are the skills that people must learn when attempting to become data scientists, so BI Analyst could be a viable alternative career path for those who enjoy analyzing data.
4. Data Product Manager
A Data Product Manager could be ideal if you want to move into a position that is less technical but still related to data science. This is a position that prefers a skill set for creating a roadmap for data-centric products or services.
The Data Product Manager role focuses on understanding current market trends and guiding data product development to meet customer needs. The position should also understand how to position the product or service as a corporate asset. At the same time, the Data Product Manager should have technical knowledge in order to communicate with technical personnel and manage the product development strategy.
A data product manager’s typical skills include business understanding, data technology understanding, and customer experience design. If the Data Product Manager is to succeed in this position, he or she must possess these skills.
5. Data Analyst
The last career path to consider is Data Analyst. Data analysts usually work with raw data to answer specific questions posed by the business. It differs from the work of BI in that, while they share skills, BI typically uses tools to create dashboards and reports to continuously track KPI and business metrics. Data analysts, on the other hand, are usually assigned to specific projects.
Data analysts frequently work in each department to provide detailed ad hoc analysis for the specific project as well as statistical analysis to gain insights from the data. Data analysts can use SQL, programming languages (Python/R), and data visualization tools, which are skills learned through data science.
If data science is not for you, there are many other careers you could pursue. You don’t want to waste the skills you’ve learned, so here are the top five data science alternative career paths to consider:
- Machine Learning Engineer
- Data Engineer
- Business Intelligence Analyst
- Data Product Manager
- Data Analyst
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