Machine Learning Engineer vs. Research Scientist

A Comparison of Machine Learning Engineer and Research Scientist Roles

Table of contents

The fields of Artificial Intelligence (AI), Machine Learning (ML), and Big Data are rapidly growing and evolving. As a result, there is a high demand for professionals with the skills and knowledge to work in these fields. Two popular career paths in these fields are Machine Learning Engineer (MLE) and Research Scientist (RS). While both roles are related to AI/ML and Big Data, they have distinct differences in their definitions, responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started.

Definitions

A Machine Learning Engineer is responsible for designing, building, and deploying ML models. They work on developing algorithms, selecting appropriate data sets, and optimizing models for performance. MLEs are focused on creating practical solutions for real-world problems and are often found in industries such as healthcare, Finance, and E-commerce.

A Research Scientist, on the other hand, is responsible for conducting research and developing new algorithms and models. They focus on pushing the boundaries of AI/ML and Big Data and are often found in academic or research institutions. RSs are responsible for publishing research papers, presenting their findings at conferences, and collaborating with other researchers.

Responsibilities

As mentioned above, MLEs focus on practical solutions for real-world problems. Their responsibilities include:

• Designing and building ML models
• Selecting appropriate data sets
• Optimizing models for performance
• Deploying models in production environments
• Monitoring and maintaining models
• Collaborating with data scientists, software engineers, and other stakeholders
• Keeping up-to-date with the latest technologies and trends in AI/ML and Big Data

On the other hand, RSs are responsible for conducting Research and developing new algorithms and models. Their responsibilities include:

• Conducting research and experiments
• Developing new algorithms and models
• Publishing research papers
• Presenting their findings at conferences
• Collaborating with other researchers
• Keeping up-to-date with the latest research in AI/ML and Big Data

Required Skills

Both MLEs and RSs require a strong foundation in Mathematics, Statistics, and Computer Science. However, there are some differences in the specific skills required for each role.

For MLEs, the required skills include:

• Strong programming skills in languages such as Python, Java, or C++
• Experience with ML libraries such as TensorFlow, PyTorch, or Scikit-learn
• Knowledge of data structures and algorithms
• Experience with cloud platforms such as AWS or Azure
• Experience with data processing tools such as Apache Spark or Hadoop
• Strong communication and collaboration skills

For RSs, the required skills include:

• Strong programming skills in languages such as Python, Java, or C++
• Experience with ML libraries such as TensorFlow, PyTorch, or Scikit-learn
• Strong mathematical and statistical skills
• Experience with research methodologies and experimental design
• Knowledge of data structures and algorithms
• Strong communication and collaboration skills

Educational Backgrounds

Both MLEs and RSs typically have a background in Computer Science, mathematics, or statistics. However, there are some differences in the specific educational backgrounds required for each role.

For MLEs, the typical educational backgrounds include:

• Bachelor's or Master's degree in computer science, Mathematics, or a related field
• Experience with ML libraries and tools
• Experience with software Engineering practices

For RSs, the typical educational backgrounds include:

• PhD in computer science, mathematics, or a related field
• Strong research experience and publications
• Experience with research methodologies and experimental design

Tools and Software Used

Both MLEs and RSs use similar tools and software for their work. These include:

• Programming languages such as Python, Java, or C++
• ML libraries such as TensorFlow, PyTorch, or Scikit-learn
• Data processing tools such as Apache Spark or Hadoop
• Cloud platforms such as AWS or Azure
• Collaboration tools such as GitHub or Jupyter Notebooks

Common Industries

MLEs are typically found in industries such as healthcare, finance, E-commerce, and technology. They work on developing practical solutions for real-world problems in these industries.

RSs, on the other hand, are typically found in academic or research institutions. They focus on pushing the boundaries of AI/ML and Big Data and are often involved in cutting-edge research projects.

Outlooks

Both MLEs and RSs have strong job outlooks. According to the US Bureau of Labor Statistics, the employment of computer and information research scientists is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. Similarly, the employment of software developers, including MLEs, is projected to grow 22 percent from 2019 to 2029, much faster than the average for all occupations.

Practical Tips for Getting Started

If you are interested in pursuing a career as an MLE or RS, here are some practical tips for getting started:

• Build a strong foundation in mathematics, statistics, and computer science
• Learn programming languages such as Python, Java, or C++
• Gain experience with ML libraries and tools such as TensorFlow, PyTorch, or Scikit-learn
• Gain experience with data processing tools such as Apache Spark or Hadoop
• Gain experience with cloud platforms such as AWS or Azure
• Participate in research projects or internships to gain practical experience
• Attend conferences and network with professionals in the field
• Keep up-to-date with the latest technologies and trends in AI/ML and Big Data

In conclusion, while both MLEs and RSs work in the same fields of AI/ML and Big Data, they have distinct differences in their definitions, responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started. It is important to consider these differences when choosing a career path in these fields.