Data science is an in-demand field, yet many aspirants often encounter challenges in landing a sought-after job due to high competition or the preference of employers for senior data scientists. However, the skills learned in data science can be used in multiple alternative careers, such as Machine Learning Engineer, Data Engineer, Business Intelligence Analyst, Data Product Manager and Data Analyst.
Machine Learning Engineers differ from data scientists in their focus on technical aspects of integrating machine learning into production. These professionals design structures and manage scaling production. While data scientists and machine learning engineers share a common base in data analysis and machine learning, the roles differ substantially, meaning those interested in transitioning would need to learn more about software engineering practices and MLOps.
Data Engineers provide valuable support for data scientists’ work. These professionals are responsible for maintaining the backend infrastructure for data tasks, including data management and storage, building data pipelines, and ensuring a stable and high-quality data stream. To become a data engineer, knowledge in SQL, database management, and big data technologies is essential.
Business Intelligence (BI) is another alternative career path. BI specialists focus on descriptive analytics and utilize data insights to formulate business initiatives based on the company’s current and historical data. This role requires skills in basic statistics, SQL and data visualization tools, and is suitable for individuals who enjoy data analysis.
For those who want to use data science skills in less technical roles, a position as a Data Product Manager could be fitting. These professionals use their understanding of market trends to guide data product development, positioning products or services as assets for their companies. This role requires skills in business understanding, data technology, and customer experience design.
The last alternate career option is to become a Data Analyst. These professionals work on a project basis, analyzing raw data to provide answers to concrete business questions, in contrast to BI professionals who continuously track KPIs and business metrics. The skills needed for this job include SQL, programming languages such as Python/R, and data visualization tools.
The options above demonstrate that the ability to analyze and extract insights from data is highly transferable and will continue to be in demand in various lines of work. Though the initial path into data science may be challenging, the skills learned offer a range of exciting and rewarding career opportunities.
