Difference between Data Scientist and Machine Learning Engineer

The advent of the digital age has brought about the rise of new professions in the tech industry, two of which are Data Scientist and Machine Learning Engineer. While both roles revolve around data and its manipulation, they encompass different sets of skills, responsibilities, and objectives. This article delves into the distinguishing features of these two professions to help you better understand their uniqueness and interplay in the broader data science ecosystem.

Understanding the Concepts: Data Science and Machine Learning

To comprehend the difference between a Data Scientist and a Machine Learning Engineer, we need to first understand the concepts of Data Science and Machine Learning.

Data Science is a comprehensive field that involves extracting meaningful insights from complex sets of data using various methods, algorithms, systems, and tools. It is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from raw data.

Data Science is “using” Machine Learning, that concentrates on creating algorithms to make predictions or decisions without being explicitly programmed to do so. It is a type of artificial intelligence (AI) that allows software applications to become increasingly precise in predicting outcomes without human intervention.

The Role of a Data Scientist

Their key responsibility is to analyze and understand specific business issues, perform feature engineering, develop, select, and fine-tune models, and subsequently generate insights to present to stakeholders.

Data Scientists are typically involved in problem formulation, translating a business problem into a data science problem, where they preprocess the data for machine learning models. Furthermore, they design and experiment with algorithms and models for the use case, and also take part in deploying the model after testing and monitoring its performance.

The Role of a Machine Learning Engineer

Machine Learning Engineers, conversely, focus on taking the models developed by the Data Scientists and scaling them out into production while ensuring adherence to business service level agreements (SLAs). They are primarily involved in maintenance, monitoring, and the integration of models into a business's workflow.

They are responsible for creating machine learning models on production-level and retraining systems, automating processes to update these models, and developing APIs for model deployment. Additionally, they also collaborate with data scientists to test machine learning models and run diagnostic tests to identify any potential issues or improvements.

There is overlap between the work of Data Scientists and Machine Learning Engineers. The main difference between the two is that Data Scientists are more focused on experimentation and story telling, while Machine Learning Engineers are stronger in programming and writing production-level code and MLOps (Machine Learning Operations).

Skills Required for Data Scientists and Machine Learning Engineers

While both Machine Learning Engineers and Data Scientists have separate functions within an organization, they possess many of the same skills and work with the same technologies. However, the emphasis on certain skills varies between the two roles.

Data Scientists should possess strong knowledge of programming languages (such as Python, R, SAS), be comfortable working with large amounts of structured and unstructured data, and have an understanding of math, statistics, and probability. They also need data visualization and data wrangling skills, knowledge of machine learning algorithms and models, as well as good communication and teamwork skills.

Machine Learning Engineers, on the other hand, need expertise in computer science, including data structures, algorithms, and architecture. They should also have a strong understanding of statistics and probability, knowledge of software engineering and systems design, very strong programming knowledge, such as Python, R, and more, and the ability to conduct data modeling, analysis, deployment and monitoring.

Differences in Career Paths

Both Data Scientists and Machine Learning Engineers play crucial roles in the data science ecosystem. However, their career paths take different trajectories.

A Data Scientist's career path is often marked by progression into senior data scientist roles, where they take on more complex projects and may have increased influence on business decisions. They may also move into management roles, leading teams of data scientists and analysts, or progress into data architect roles, designing the systems that handle an organization's data.

Contrarily, Machine Learning Engineers may advance into senior roles where they oversee large-scale projects and make key decisions about the systems they're developing. They may also transition into roles like Data Architect, designing the systems that handle an organization's data, or AI Architect, designing AI elements of systems.

Current Industry Trends

The demand for both Data Scientists and Machine Learning Engineers has seen a significant surge. Today, Data Scientist and Machine Learning Engineer are among the top AI jobs that pay at least $95K.

Data science has become a mainstream profession, and data science professionals are in high demand across organizations of all sizes and sectors, from big tech companies to traditional businesses. However, the role of a Machine Learning Engineer is gaining prominence as companies realize that the value of data science cannot be realized until a model is successfully deployed to production.

While data science and model development are still lucrative roles across industry and academia, in recent years the focus in the industry has slightly shifted to building scalable and reliable infrastructure to serve data science models to millions of customers.

The Transition from Data Scientist to Machine Learning Engineer

Given the overlap between the two roles, it is possible for Data Scientists to transition to Machine Learning Engineering roles. The most pertinent skills that Data Scientists need to learn to become effective Machine Learning Engineers include software engineering, the ability to write optimized and production-level code, rigorous testing, understanding, building, and operating existing or custom tools, and platforms for reliable model deployment and management.

However, since companies require Machine Learning Engineers to have prior relevant experience, it becomes practically infeasible for Data Scientists to justify a Machine Learning profile if they do not have real-world hands-on experience in industry settings. Therefore, the best avenue for existing Data Scientists to transition to Machine Learning Engineering is with their current employer.

Summary

Whether you're a data scientist or a machine learning engineer, the future of these professions looks bright. With the rapid advancement in technology and the increasing reliance on data-driven decision-making, the demand for skilled professionals in these areas is only expected to grow.

The choice between a career as a Data Scientist or a Machine Learning Engineer depends on your interests, skills, and career goals. If you enjoy delving deep into data, discovering patterns, and generating insights, a career as a Data Scientist could be ideal for you. On the other hand, if you're more inclined towards software engineering, creating algorithms, and working on the deployment, monitoring and optimization of models, a career as a Machine Learning Engineer might be the right path for you.