The transition to a digital landscape is tough for businesses struggling to use their data to achieve a competitive advantage. Businesses need data-driven strategies to tap into the power of data, thereby increasing the demand for data experts. The machine learning engineer vs. data scientist debate is an outcome of the growing demand for data in businesses.
Professionals who can help businesses make the most of their data are likely to enjoy promising career benefits. However, the similarities between the jobs of machine learning engineers and data scientists create misconceptions about the roles.
On the other hand, the two roles are associated with different responsibilities and demand different skillsets. For example, machine learning engineers have the special skills to deploy machine learning models. Data scientists have a broad range of skills related to data collection and analytics. Let us explore the notable differences between an ML engineer and a data scientist to identify the best career path.
Why Should You Compare an ML Engineer with a Data Scientist?
The best answer to such questions would point to the changing dynamics of the labor market for data-centric job roles. The growing adoption of AI systems has led to the expansion of the global machine-learning market, which might grow beyond $170 billion by 2030.
It is also important to note that the debate on data scientists vs. machine learning engineers revolves around the significance of data scientists. The US Bureau of Labor Statistics has predicted that the demand for data scientists will grow by 35% from 2022 to 2032.
You can notice that both career paths are equally promising and can help you enjoy the best rewards of professional development. Therefore, a detailed comparison of the responsibilities, expected salary estimate, skills, and technology required for the two roles is an essential priority for many professionals.
What are the Differences in the Career of ML Engineers and Data Scientists?
The decision to pursue a career as a machine learning engineer or a data scientist is a difficult one, considering the strong demand for data experts. On top of it, some people ignore the data scientist and machine learning engineer difference due to blurred lines between the roles. For example, general assumptions about data scientists may suggest that they only work with data analytics, and ML engineers can only build models. On the contrary, you would find a lot of differences between the two roles and discover that they require a special set of skills. Here is an overview of the differences between a career as a machine learning engineer and a data scientist.
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What are the Responsibilities of ML Engineers and Data Scientists?
The foremost point for comparison between the roles of machine learning engineers and data scientists is the outline of responsibilities. What would you have to do every day on the job as a machine learning engineer or a data scientist?
Interestingly, machine learning engineers and data scientists have common responsibilities and fundamental skills. For example, machine learning engineers and data scientists must have a strong command over programming languages like Python for effective data analysis and manipulation. On top of it, ML engineers and data scientists must have an in-depth understanding of important machine learning frameworks required for the efficient development and deployment of ML models.
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Responsibilities of Data Scientists
Data scientists are responsible for extracting insights from data to help in driving business decisions. The job responsibilities of data scientists can provide the right explanation for queries like “Who earns more, data scientist or machine learning engineer?” by emphasizing the special skills required to address them.
The responsibilities of data scientists include data collection and cleaning, algorithm selection, experimentation, and custom model development. Data scientists collect data from different sources and refine it through preprocessing techniques alongside achieving data integrity and quality. Data scientists also identify the right data patterns to identify the best data structures and ML algorithms. In some cases, data scientists also design machine learning models according to unique business challenges.
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Responsibilities of Machine Learning Engineers
The most crucial responsibility of machine learning engineers revolves around deploying artificial intelligence models to production at scale. The salary comparison between an ML engineer and a data scientist should also draw the limelight on the responsibilities of machine learning engineers. ML engineers have to implement processes such as rigorous testing, model deployment, and model optimization.
In addition, machine learning engineers must also ensure that ML systems can manage the growing volume of user interactions and data. ML engineers must also implement rigorous and frequent testing of models to ensure better accuracy in real-world applications. On top of that, machine learning engineers must optimize the models to ensure better user experiences.
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What are the Skills Required for ML Engineers and Data Scientists?
The next crucial point of comparison between a machine learning engineer and a data scientist is the outline of skills required for the roles. The machine learning engineer vs. data scientist debate draws many similarities between the skills required for the jobs. However, you would find subtle differences between the skills required for ML engineer and data scientist jobs.
Data scientists need skills such as problem-solving, statistics, data science, and data analytics and data visualization techniques. They share a similar skill requirement with machine learning engineers, programming. In addition, they must also learn about the fundamentals of machine learning techniques such as supervised and unsupervised learning. Data scientists also need presentation skills and written and verbal communication skills.
On the other hand, the data scientist vs. machine learning engineer comparison must draw your attention to the specialized skills of ML engineers. Machine learning engineers must know about data structures, software engineering, data modeling, and statistics. On top of it, ML engineers must have in-depth conceptual knowledge of machine learning and different popular machine learning frameworks.
What is Tech Stack Used by ML Engineers and Data Scientists?
Another important point for comparing the careers of ML engineers and data scientists revolves around the tech stack used in both roles. The tech stack is an important factor in determining answers to “Who earns more data scientist or machine learning engineer?” due to the specialization required for using the technologies. Interestingly, you would find many common additions in the tech stack for ML engineers and data scientists. For example, machine learning engineers and data scientists must be fluent in Python, Git, AWS, and Azure. They must also know about algorithms and neural networks and have the skills to develop advanced ML models.
Data scientists would need special skills in programming languages like R and SQL for data mining, statistical modeling, and database management. Data scientists must also know Microsoft Excel and its uses for basic analytics, data refining tasks, and initial data exploration.
Matplotlib is also an important addition to the tech stack of data scientists as it helps with data visualization. Data scientists also use Jupyter notebooks for interactive data analysis and collaboration. Most importantly, data scientists also leverage Python libraries such as Scikit-learn to create and deploy ML models.
The difference between data science and machine learning engineering also draws attention to the specialized tools included in the tech stack for ML engineers. For example, ML engineers must know C++ to optimize and implement high-performance ML algorithms. Machine learning engineers must be fluent in using Linux OS, the most popular operating system for ML workloads.
ML engineers should also know the best practices for using deep learning frameworks such as TensorFlow and PyTorch. In addition, ML engineers must also have practical expertise in frameworks such as TorchServe that help in the efficient deployment and management of ML models.
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What is the Salary of ML Engineer and Data Scientist?
The most important aspect in the comparison between career of ML engineers and data scientists is the salary. ML engineers and data scientists have a specialized skill set thereby boosting their expected salary estimates. The ML engineer vs. data scientist salary comparison is important as both the roles have a strong demand and offer higher earning potential than other jobs. Apart from the salary, machine learning engineers and data scientists enjoy many other benefits from their professional growth.
The average annual salary for an entry-level data scientist is $103,500.
The average annual salary of an ML engineer is almost $136,600.
It is important to remember that the salary of an ML engineer or a data scientist would vary according to experience, industry and location. In addition, valuable skills and business acumen can push a professional towards new heights of success in both the roles.
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Final Words
The comparison between the careers of machine learning engineers and data scientists reveals that both of them are in-demand roles. As the machine learning engineer vs. data scientist debate gains momentum, it is important to identify the most relevant points of comparison. Some of the notable aspects for comparison include responsibilities, skill requirements, tech stack, and salary for the roles. The comparison between the two roles can help an aspiring professional find the right career path aligned with their career goals and existing skills. Find the best training resources for ML engineer and data scientist roles for a highly rewarding career now.
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