Research Scientist vs. Lead Machine Learning Engineer

Research Scientist vs. Lead Machine Learning Engineer: A Comprehensive Comparison

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The fields of artificial intelligence (AI), machine learning (ML), and Big Data have been growing rapidly in recent years, and with that growth comes an increasing demand for skilled professionals. Two such roles that are in high demand are research scientists and lead machine learning engineers. While both positions require expertise in AI/ML and big data, there are key differences in their responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers. In this article, we will explore these differences in detail to help you make an informed decision about which career path to pursue.

Definitions

Before we dive into the differences between these two roles, let's first define what they are.

Research Scientist

A Research scientist is a professional who conducts research in a specific field, such as AI/ML, with the goal of advancing knowledge and developing new technologies. In the context of AI/ML, a research scientist may work on developing new algorithms, improving existing ones, or exploring new applications of AI/ML.

Lead Machine Learning Engineer

A lead machine learning engineer is a professional who designs, develops, and deploys machine learning systems in commercial or Industrial settings. They are responsible for building and maintaining ML models that can be used to solve real-world problems, such as predicting customer behavior, optimizing supply chains, or detecting fraud.

Responsibilities

While both research scientists and lead Machine Learning engineers work in the AI/ML and big data space, their day-to-day responsibilities differ significantly.

Research Scientist

The primary responsibility of a research scientist is to conduct research. This involves designing experiments, collecting and analyzing data, and publishing papers in academic journals. Research scientists may also be responsible for mentoring junior researchers, applying for grants, and presenting their findings at conferences.

Lead Machine Learning Engineer

The primary responsibility of a lead machine learning engineer is to design, develop, and deploy machine learning systems in commercial or industrial settings. This involves collaborating with cross-functional teams to identify business problems that can be solved with ML, selecting appropriate algorithms and models, training and Testing the models, and deploying them in production environments. Lead machine learning engineers may also be responsible for managing a team of engineers, monitoring and maintaining the ML systems, and optimizing their performance.

Required Skills

Both research scientists and lead machine learning engineers require a strong foundation in AI/ML and big data, but there are some key differences in the skills they need to be successful in their roles.

Research Scientist

To be a successful research scientist, you need:

• Strong mathematical and statistical skills
• Proficiency in programming languages such as Python, R, and Matlab
• Knowledge of algorithms and data structures
• Familiarity with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras
• Strong problem-solving and critical thinking skills
• Excellent written and verbal communication skills
• Ability to work independently and collaboratively

Lead Machine Learning Engineer

To be a successful lead machine learning engineer, you need:

• Strong programming skills in languages such as Python, Java, or C++
• Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Knowledge of software Engineering principles and best practices
• Familiarity with cloud computing platforms such as AWS, Azure, or Google Cloud
• Strong problem-solving and critical thinking skills
• Excellent communication and leadership skills
• Ability to work independently and collaboratively

Educational Backgrounds

Both research scientists and lead machine learning engineers require a strong educational background in Computer Science, mathematics, or a related field. However, the specific degrees and certifications that are most valuable differ between the two roles.

Research Scientist

To become a research scientist, you typically need:

• A Ph.D. in computer science, Mathematics, statistics, or a related field
• Experience conducting research and publishing papers in academic journals
• Familiarity with the latest research in AI/ML and related fields

Lead Machine Learning Engineer

To become a lead machine learning engineer, you typically need:

• A bachelor's or master's degree in computer science, mathematics, or a related field
• Experience building and deploying ML systems in commercial or industrial settings
• Certifications in ML frameworks or cloud computing platforms, such as AWS Certified Machine Learning - Specialty or Google Cloud Certified - Professional Machine Learning Engineer

Tools and Software Used

Both research scientists and lead machine learning engineers use a variety of tools and software in their work, but the specific tools they use differ based on their responsibilities.

Research Scientist

Tools and software commonly used by research scientists include:

• Programming languages such as Python, R, and MATLAB
• Deep learning frameworks such as TensorFlow, PyTorch, and Keras
• Data visualization tools such as Matplotlib and Seaborn
• Statistical analysis tools such as SPSS and SAS
• Cloud computing platforms such as AWS and Google Cloud

Lead Machine Learning Engineer

Tools and software commonly used by lead machine learning engineers include:

• Programming languages such as Python, Java, or C++
• ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Cloud computing platforms such as AWS, Azure, or Google Cloud
• Containerization tools such as Docker and Kubernetes
• Data storage and processing tools such as Apache Hadoop and Spark

Common Industries

Research scientists and lead machine learning engineers work in a variety of industries, but there are some industries where one role is more common than the other.

Research Scientist

Industries where research scientists are commonly employed include:

• Academia
• Research institutions
• Government agencies
• Technology companies

Lead Machine Learning Engineer

Industries where lead machine learning engineers are commonly employed include:

• Technology companies
• Financial services
• Healthcare
• Retail and E-commerce
• Manufacturing

Outlooks

Both research scientists and lead machine learning engineers have strong job outlooks, with high demand and competitive salaries.

According to the Bureau of Labor Statistics, 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. The median annual wage for computer and information research scientists was $126,830 in May 2020.

According to Glassdoor, the national average salary for a lead machine learning engineer is $153,000 per year, with salaries ranging from $105,000 to $205,000 depending on experience, location, and industry.

Practical Tips for Getting Started

If you are interested in pursuing a career as a research scientist or lead machine learning engineer, here are some practical tips to help you get started:

Research Scientist

• Pursue a Ph.D. in computer science, mathematics, or a related field
• Gain research experience by working as a research assistant or intern
• Attend academic conferences and publish papers in academic journals
• Build a strong network of colleagues and mentors in your field

Lead Machine Learning Engineer

• Earn a bachelor's or master's degree in computer science, mathematics, or a related field
• Gain experience by working on ML projects, either in academic settings or through internships
• Obtain certifications in ML frameworks or cloud computing platforms
• Build a strong portfolio of ML projects and showcase your work on platforms such as GitHub or Kaggle

Conclusion

In summary, research scientists and lead machine learning engineers are both important roles in the AI/ML and big data space, but they have different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started. By understanding these differences, you can make an informed decision about which career path to pursue and take the necessary steps to build your skills and experience in the field.