Machine Learning Engineer vs. Data Scientist: Which Role is Right for You?

In today’s rapidly evolving tech-driven industries, two pivotal professions stand out – machine learning engineers and data scientists. They uncover the hidden value in data, driving innovation and progress across industries like healthcare, finance, and technology. But while their paths cross, their skills and duties set the two apart.

This comprehensive comparison illuminates their core functions, skills, and career journeys. Whether you’re considering a career in machine learning vs data science, this article can serve as your guide. So, let’s explore the world of data-driven expertise.

Who Is a Machine Learning Engineer?

Machine learning engineers (MLEs) are builders who take machine learning models from the drawing board and transform them into real-world applications. Their primary responsibility is to design, develop, and deploy machine learning models that are both efficient and scalable. This means ensuring the algorithms work correctly, testing them for accuracy, and optimizing them for performance. 

They collaborate closely with data scientists, who might conceptualize the models, but it’s the MLE who ensures these models can run in real-world environments. Think of them like bridge builders: while someone might design the bridge, the MLE ensures it’s constructed sturdily and can handle traffic. 

Other duties include fine-tuning models based on real-world feedback, integrating these models into applications and systems, and often working with big data technologies to handle the vast amounts of data these models require.

Who Is a Data Scientist?

A data scientist is often seen as the detective in the world of technology. They take raw data and try to uncover insights and patterns. They specialize in analyzing and interpreting complex datasets, aiming to solve specific problems or discover patterns that might not be immediately obvious. 

Their toolkit includes a mix of statistics, data mining, and machine learning techniques, combined with the expertise to use them effectively. While they do build machine learning models like machine learning engineers (MLEs), their primary focus is more on the ‘why’ and ‘what’ rather than the ‘how.’ This means they’re responsible for understanding the problem at hand, choosing the right data, and picking or even designing the best model or method to address the issue. 

Once they have their insights or models, they communicate their findings in a clear, concise manner to stakeholders, ensuring decisions are made based on data-driven evidence. 

Imagine a retail company that wants to boost its sales. They’ve collected data over the years – purchase histories, customer reviews, website clicks, and more, but aren’t sure what to do with it. 

The data scientist will first clarify the problem: “How can we increase sales using the data available?” They then dive into the ocean of data. After cleaning and organizing it, they find that younger customers tend to buy more during certain promotional periods, while older ones buy when given loyalty discounts. With the help of an MLE, the data scientist creates a machine-learning model that predicts that offering age-specific promotions could boost sales by 15%.

Machine Learning vs. Data Science

Within the broader landscape of data science vs machine learning, ML plays a pivotal and specialized role. Data science is all about working with data, from gathering and cleaning it to figuring out what it means through analysis.

Machine learning, on the other hand, is about creating prediction models. These models make guesses based on data. But making these models really good at guessing takes time and experimenting. 

It’s also worth highlighting that data science is a multidisciplinary field that incorporates elements of statistics, mathematics, computer science, and domain knowledge. 

Responsibilities of ML Engineer vs. Data Scientist

Let’s take a look at the distinct responsibilities between ML engineers and data scientists.

Machine Learning Engineer:

• Craft intricate machine learning models and embed them in practical applications.
• Tailor algorithms for optimal performance and collaborate closely with software engineers for seamless integration.
• Weave together innovation and practicality to turn theoretical models into functional realities.

Data Scientist:

• Meticulously scrutinize data to uncover hidden patterns and valuable insights.
• Develop elaborate statistical models and sophisticated machine-learning solutions.
• Translate discoveries into understandable information for non-data scientists.
• Act as a data custodians, ensuring accuracy and reliability through data pre-processing. Data pre-processing is the process of cleaning, organizing, and preparing data for analysis by removing errors, inconsistencies, and irrelevant information.

Skills Required for Machine Learning Engineers and Data Scientists

Just like requirements, skills associated with ML engineers and data scientists may vary. Let’s take a look at a few for each profession.

Machine Learning Engineers:

• Programming Proficiency: Machine learning engineers need to be proficient in programming languages like Python and Java, as these are the foundation for building and implementing machine learning models.
• Library Mastery: Data scientists need to have mastery over machine learning libraries like TensorFlow and PyTorch because these libraries provide the tools and frameworks required to design, train, and optimize complex machine learning models. They offer high-level abstractions, efficient computation, and GPU acceleration, making it possible to work with large datasets and intricate neural network architectures,
• Software Engineering: A strong foundation in software engineering principles is required for creating robust and scalable machine learning systems. This includes understanding version control, code organization, and collaboration with other software engineers.
• Model Deployment: Machine learning engineers should have the ability to deploy models into production systems. This involves integrating models into larger software architectures and ensuring they can handle real-world data and requests. This involves having skills in containerization technologies like Docker, knowledge of cloud platforms like AWS or Azure, experience with deployment pipelines, and proficiency in monitoring and scaling strategies to maintain model performance in a production environment.
• Ethical Considerations: Machine learning engineers should have an understanding of data ethics and privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations impose strict guidelines on the collection, storage, and usage of personal and sensitive data, requiring ML engineers to ensure that their models and systems comply with these laws and protect individuals’ privacy rights.

Data Scientists:

• Statistical Analysis: Data scientists require a strong proficiency in statistical analysis and hypothesis testing. This allows them to derive meaningful insights from data and make informed decisions.
• Data Visualization: Skillful use of data visualization tools like Matplotlib and Seaborn is advised. Visualization helps data scientists communicate complex patterns and insights to both technical and non-technical audiences.
• Machine Learning Algorithms: Command over a range of machine learning algorithms like linear regression, random forest, and convolutional neural networks (CNNs) is key for data scientists. Different problems may require different algorithms, and data scientists should be able to select and apply the most suitable ones.
• Domain-Specific Knowledge: Domain-specific knowledge is necessary for effective analysis. Understanding the context and nuances of the data, such as industry-specific jargon, regulatory requirements, and market trends, helps data scientists uncover relevant insights and ask the right questions.
• Effective Communication: Data scientists often need to convey their findings to non-technical stakeholders, such as managers and decision-makers. Effective communication skills come in handy for presenting complex results in a clear and understandable manner.
• Ethical Awareness: Similar to machine learning engineers, data scientists must also be aware of data ethics and privacy concerns. They should handle data responsibly, considering biases, potential implications, and issues like personally identifiable information (PII) protection, consent for data usage, and algorithmic fairness in their analysis and recommendations.

Machine Learning Engineer vs Data Scientist Salary

Salaries for both data scientists and machine learning engineers can vary widely based on experience, location, industry, and the specific company. Generally, data scientists and machine learning engineers command competitive salaries due to the high demand for their specialized skills. Here’s an overview of the average machine learning engineer salary vs a data scientist salary:

Data Scientists:

• Entry-Level: In the United States, entry-level data scientists can expect an average salary ranging from $70,000 to $100,000 per year.
• Mid-Level: Data scientists with four to six years of experience typically earn an average salary of $100,000 to $130,000 per year.
• Senior-Level: Senior Data Scientists with 10 to 15 years of experience and proven expertise can earn an average salary ranging from $130,000 to $170,000 or more annually.

Machine Learning Engineers:

• Entry-Level: Entry-level machine learning engineers often earn an average salary in the range of $80,000 to $110,000 per year.
• Mid-Level: With two to five years of experience, mid-level machine learning engineers can expect to earn around $110,000 to $150,000 per year.
• Senior-Level: Senior machine learning engineers with five to 10 years of experience and a track record of successful projects can command salaries ranging from $150,000 to $180,000 or higher annually.

Conclusion

Machine learning engineers and data scientists are foundations in the ever-changing realm of data-driven advancements. Although they share common ground, their expertise and main responsibilities differ significantly. Choosing the right path between these roles depends on personal preferences, skills, and aspirations that align with their distinct qualities.

FAQ

We’ve also compiled a list of frequently asked questions regarding both fields.

Do Machine Learning Engineers Earn More Than Data Scientists?

Machine learning engineer  vs data scientist salaries can vary based on location, industry, experience, and specific job responsibilities. In some cases, machine learning engineers might earn slightly more due to their specialized focus on implementing and optimizing machine learning models. However, note that the salary difference may not be significant, and both roles often offer competitive compensation.

What Kind of Educational Background is Preferred for a Data Scientist?

Data science is a field that draws from multiple disciplines. A strong foundation in quantitative analysis is key, and a preferred educational background for a data scientist includes a degree in fields like computer science, statistics, mathematics, or engineering. This ensures a solid understanding of the concepts and tools necessary to wrangle data,  conduct analysis, and build models. Many data scientists further their studies with master’s or Ph.D. degrees, allowing them to delve deeper into specific areas of data science.

In Which Industries or Domains Do Machine Learning Engineers Usually Work?

Machine learning engineers can work across various industries and domains. They are in demand in fields like finance, healthcare, e-commerce, technology, automotive, entertainment, and more. Any industry that can benefit from utilizing data to make predictions, automate tasks, or improve decision-making is likely to employ machine learning engineers.

Some emerging fields for machine learning engineers:

• Quantum Computing: ML engineers create algorithms for quantum computers, useful in cryptography and optimization.
• Edge Computing: ML engineers deploy lightweight models on IoT devices for real-time data processing.
• Climate Science: ML aids climate modeling and environmental monitoring for predictions and impact assessments.
• Ethical AI: ML engineers specialize in fairness-aware algorithms and ethical AI practices.
• Robotics: ML integration in self-driving cars, warehouse automation, and autonomous systems.
• Healthtech: ML in digital therapeutics, drug discovery, and personalized medicine.
• Agtech: ML optimizes crop management and disease detection in agriculture.
• Space Exploration: ML aids satellite data analysis, spacecraft navigation, and celestial object identification.

Can a Person Transition From Being a Machine Learning Engineer to a Data Scientist or Vice Versa?

Yes, transitioning between roles is possible and quite common, as both roles involve translatable skills. A machine learning engineer can transition to a data scientist role by enhancing their skills in exploratory data analysis, statistical modeling, and business understanding. Similarly, a data scientist can transition to a machine learning engineer role by focusing more on the engineering aspects of deploying and maintaining machine learning models.

Are There Any Certifications or Courses That Can Help in Pursuing a Career as a Machine Learning Engineer vs Data Scientist?

Certainly, there are several certifications and courses that can enhance your skills in these roles:

For Machine Learning Engineers:

• Coursera’s Machine Learning Specialization: Offered by Stanford University, this series of courses covers fundamental machine learning concepts and techniques.
• TensorFlow Developer Certificate: Google’s certification program validates proficiency in using TensorFlow for building and deploying machine learning models.
• Fast.ai Practical Deep Learning for Coders: This course offers practical, hands-on experience in deep learning techniques.

For Data Scientists:

• Coursera’s Data Science Specialization: This series of courses covers data science concepts, including data analysis, machine learning, and data visualization.
• IBM Data Science Professional Certificate: This program provides training in data analysis, machine learning, and data visualization using tools like Python and R.
• edX’s Microsoft Professional Program in Data Science: This program covers various aspects of data science, including machine learning and big data analysis.