Data Scientist vs Machine Learning Engineer

In this increasingly data-driven world, you’ve definitely heard of data scientists and machine learning engineers. But what are the real differences between these roles?

While there's significant overlap, and you'll likely tackle tasks from the entire data pipeline in either position, it's important to understand the key distinctions if you're applying for data-related jobs.

Both professionals work with data and contribute to AI advancements, but their focus, skill sets, and day-to-day responsibilities differ.

This article explores the crucial differences between the roles of data scientist vs machine learning engineer, their job descriptions, required skills, and the education necessary to excel in these rapidly expanding fields.

Through our analysis of 2,000 job listings on Indeed USA, we reveal current job market trends and employer requirements—aiming to guide individuals considering careers in these dynamic and rewarding fields.

Table of Contents

• Roles and Responsibilities
• Skills
• Education
• Salary
• Career Path
• Key Differences
• How to Become a Data Scientist
• How to Become a Machine Learning Engineer
• FAQs

Data Scientist vs Machine Learning Engineer: Roles and Responsibilities

Here, we'll explore the similarities and differences between data scientist and machine learning engineer roles and responsibilities.

What Is a Data Scientist?

Data scientists direct strategic business choices. They utilize statistics, computer science principles, and machine learning algorithms to derive essential insights from data, guiding organizational decision-making.

When comparing the roles of data scientist vs ML engineer, many responsibilities overlap. But the main difference is that data scientists focus on converting intricate business problems into manageable data-driven questions. They carefully analyze data to identify patterns and develop models that predict future behaviors or trends.

Data scientists are adept at clearly and effectively communicating complex results. They frequently use data visualization and storytelling to ensure that stakeholders comprehend and leverage the insights provided.

In short, their primary goal is to influence decision-making processes.

What Is a Machine Learning Engineer?

Machine learning engineers focus on designing, building, and deploying machine learning models at scale. They bridge the gap between data science and software engineering, translating theoretical models into practical, efficient, and scalable solutions.

The role of a machine learning engineer involves developing algorithms and implementing machine learning systems. They work on optimizing machine learning models for production environments, ensuring they can handle large volumes of data and operate efficiently in real-world scenarios.

Machine learning engineers also have a significant role in model deployment and maintenance. They implement strategies for efficient model integration, monitoring, and updating across the organization's systems and platforms.

This role’s primary focus is on operationalizing machine learning models.

While their main focuses may differ, machine learning engineers' duties can overlap with data scientists' work—especially in organizations where the entire machine learning lifecycle is managed by a single team.

Data Scientist vs Machine Learning Engineer: Skills

Now that we know the major differences between ML engineers vs data scientists, let’s look at the specific skills used by these roles, based on our job market research.

Programming Skills

Both data scientists and ML engineers require a strong foundation in programming. But the specific languages and technologies employers desire vary, reflecting the unique requirements of each specialization.

Our data reveals distinct patterns that reflect the specific needs and focuses of each role in 2024.

Data Scientists:

• Python (56.7%): Essential for data manipulation, visualization, and ML
• R (33.0%): Valued for statistical analysis and visualization
• SQL (30.4%): Important for data querying
• Java (9.3%): Used for specific data-intensive tasks

Machine Learning Engineers:

• Python (77.4%): Dominant for ML libraries and frameworks
• SQL (26.1%): Crucial for data pipeline management
• Java (22.8%): Preferred for performance and scalability in production
• R (20.5%): Useful for specific statistical models

Comparison

While Python is a staple in both fields, R's importance in data science vs machine learning engineering stems from its specialized use in statistics and data visualization, areas less emphasized in ML engineering.

On the other hand, Java's strengths in performance, scalability, and enterprise-level application development make it more valuable for ML engineering. Machine learning engineers often need to integrate ML models into large-scale production systems, where Java's capabilities shine. Data scientists, however, typically prioritize analytical and exploratory capabilities over these production-oriented traits.

If you’re looking to enter these fields, you should focus on mastering Python as a foundational language. Those aiming for data science should also invest time in learning R, while aspiring ML engineers should consider Java as a secondary language. Regardless of the specific role, you’ll need a strong understanding of SQL for working with databases and managing data pipelines effectively.

Job-Specific Skills

The distinction between data scientists vs machine learning engineers isn't easily defined by separate skill sets. These roles often utilize the same general skills and tools, but with varying emphasis. While not exclusive to either role, the following skills are more commonly associated with and frequently used by professionals in each respective field.

Data Scientists:

• Data Visualization: Master tools like Tableau and Power BI to create clear, engaging dashboards and reports that effectively communicate findings to stakeholders.
• Data Mining: Extract valuable insights from large datasets by identifying patterns, anomalies, and correlations crucial for strategic decision-making.
• Statistical Analysis: Apply statistical methods to interpret data, validate assumptions, and support business decisions with solid evidence.
• Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand data structure, identify errors, check assumptions, and form hypotheses before deeper analysis.
• A/B Testing: Design and interpret experiments to optimize products, services, and user experiences based on data-driven results.
• Hypothesis Testing: Perform rigorous tests to confirm or reject hypotheses, ensuring the validity of data-derived findings.

ML Engineers:

• Algorithm Development: Design and implement advanced machine learning algorithms tailored to specific problems.
• Model Optimization: Tune models to achieve the best performance possible, including techniques like parameter tuning, feature selection, and using novel optimization algorithms.
• Deep Learning: Apply deep learning frameworks such as TensorFlow and PyTorch to develop complex models, particularly useful in applications like image and speech recognition.
• Reinforcement Learning: Design systems that learn to make decisions through trial and error, primarily used in gaming, robotics, and navigation systems.
• Distributed Computing: Utilize platforms like Apache Spark to process and analyze large datasets that cannot be handled by a single machine, essential for training large models efficiently.
• Model Deployment: Deploy machine learning models into production environments, ensuring they run efficiently and scale according to demand.
• Automation and Pipelining: Build and maintain pipelines that automate the flow of data through different stages of processing and modeling, crucial for the continuous deployment of machine learning models.
• Containerization and Orchestration: Use technologies like Docker and Kubernetes to containerize machine learning applications and manage their deployment across different environments.

Overlapping Skills

In this section of our data scientist vs ML engineer comparison, we identify the skills where the roles converge.

AI Skills

Data Scientists:

• Machine Learning (69.3%): Apply existing algorithms for predictive modeling and data analysis.
• AI (21.2%): Create automated decision-making processes and enhance analytical models.
• NLP (19.0%): Extract insights from text data (e.g., social media, customer feedback).
• Deep Learning (11.7%): Complex modeling of unstructured data (images, text).
• APIs (3.5%): Deploy models and retrieve data from various sources.

Machine Learning Engineers:

• Machine Learning (88.3%): Design, build, and implement ML systems with complex algorithms.
• AI (60.4%): Develop systems for computer vision, NLP, and autonomous decision-making.
• Deep Learning (24.8%): Focus on image and speech recognition applications.
• NLP (21.4%): Create chatbots, sentiment analysis tools, and language translation systems.
• APIs (9.1%): Connect ML models with other applications for scalable production systems.

Comparison

While they share similar AI skills, there are differences between data scientist and machine learning engineer roles.

ML engineers require a deeper understanding of AI and ML concepts, reflected in the higher percentages across all skills. Taking a closer look, they also have a more technical focus, dealing with the intricacies of building and deploying ML systems.

Data scientists, on the other hand, lean more towards the analytical side. They use these skills to extract insights from data and solve business problems. Their focus is on applying ML and AI techniques to derive valuable insights, rather than building the underlying systems.

In essence, while data scientists use these skills to analyze and interpret data, ML engineers use them to build and implement the systems that make such analysis possible at scale.

AI Tools

Here, we compare the specific AI tools required in machine learning engineer vs data scientist job postings.

The data reveals a difference in the adoption of AI tools between ML engineers vs data scientists. Machine learning engineers show a much higher rate across all tools, which aligns with their role's focus on building and implementing AI systems.

PyTorch and TensorFlow emerge as the dominant tools for both roles, but with a larger emphasis in machine learning engineers. While data scientists use these tools primarily for analysis and prototyping, ML engineers leverage them for building production-ready AI systems.

Interestingly, scikit-learn—often associated with more traditional machine learning techniques—shows a smaller gap between the two roles. This could indicate that both data scientists and ML engineers still rely on classical machine learning methods alongside deep learning approaches.

If you're aiming for a career as a data scientist or machine learning engineer, focus on mastering PyTorch and TensorFlow—these are essential tools for both roles. Also, get familiar with scikit-learn and Keras as complementary tools. Remember, hands-on experience is crucial, especially for ML engineering positions. So, start practicing!

ML Algorithms and Methods

In this section, we’ll compare the ML techniques required in data science vs machine learning.

The data reveals significant differences between data scientists and machine learning engineers in the focus and depth of algorithm usage.

ML engineers show a much higher need for advanced techniques, particularly in deep learning. This aligns with their role in building and implementing complex AI systems. They also have a stronger focus on computer vision, likely due to the increasing demand for visual AI applications.

Data scientists—while using similar algorithms—show a more balanced distribution across different methods. Their higher emphasis on NLP suggests a focus on text-based data analysis, which is crucial for extracting insights from unstructured data sources like social media, customer feedback, and business documents.

Aspiring data scientists and ML engineers should prioritize deep learning skills. Data scientists should focus on NLP and a broad skill set, while ML engineers should specialize in advanced techniques, particularly deep learning and computer vision. When comparing data scientists vs machine learning engineers, it’s evident that both roles benefit from a well-rounded knowledge of ML algorithms and methods.

Cloud Skills and Software

Cloud skills are becoming increasingly critical as data moves to the cloud. Here, we’ll compare the necessary cloud and containerization technologies required in ML engineer vs data scientist job postings.

Data scientists show a clear preference for Microsoft Azure and AWS, focusing primarily on data storage and processing capabilities. Their lower usage of containerization and orchestration tools (Docker and Kubernetes) suggests less involvement in deploying and scaling applications.

In contrast, ML engineers demonstrate a more balanced and extensive use of cloud and containerization technologies. Their higher adoption of Docker and Kubernetes indicates a stronger focus on creating scalable, reproducible environments for ML models. This aligns with their role in building and deploying production-ready ML systems.

If you're aiming to be a machine learning engineer, focus on learning Docker, Kubernetes, and cloud platforms. For aspiring data scientists, cloud skills are important, but you'll focus less on deployment. Regardless of the differences between data scientist and machine learning engineer roles, everyone should familiarize themselves with major cloud platforms, especially Azure and AWS, to gain a competitive edge.

Data Scientist vs Machine Learning Engineer: Education

Let's examine a breakdown and comparative analysis of the top degrees mentioned in job postings for machine learning engineer vs data scientist:

Top Degrees for Data Scientists

1. Data Science (47.4%)
2. Engineering (22.6%)
3. Mathematics (21.8%)
4. Computer Science (18.0%):
5. Statistics (17.2%)

Top Degrees for Machine Learning Engineers

1. Engineering (68.9%)
2. Computer Science (61.6%)
3. Machine Learning (57.1%)
4. Data Science (31.2%)
5. Statistics (21.9%)

Comparison

• Engineering is a top choice for both but is significantly more emphasized for ML engineers. It reflects the engineering-oriented tasks of building and implementing machine learning systems.
• Computer science also sees heavy emphasis in both fields but is slightly more prevalent among ML engineers, aligning with the technical demands of the role.
• Data science is, unsurprisingly, the most crucial degree for data scientists, while machine learning is highly sought-after for ML engineers.
• Statistics is vital in both fields but is more emphasized for data scientists, which fits with the role's stronger focus on data analysis compared to the more application-focused nature of machine learning engineering.
• Degrees like chemistry (5.3% for Data Scientists) and architecture (3.6% for Data Scientists) indicate a broader, more interdisciplinary approach in data science roles, potentially pointing to niche areas where data science applications are emerging.

In conclusion, there are key differences between data scientist and machine learning engineer educational pathways:

• Data Science: Focus on data science, mathematics, or statistics degrees
• ML Engineering: Prioritize engineering, computer science, or specialized ML programs
• Both: Consider additional computer science courses or certifications

Remember, while formal education is crucial, supplement your degree with practical experience and continuous learning in these rapidly evolving fields.

The tech industry increasingly values practical skills over formal degrees. Many professionals transition into data science and ML engineering through online courses and certifications, which offer hands-on experience and up-to-date knowledge at a lower cost than traditional education.

These options are ideal for career changers and those looking to upskill quickly. Explore our 365 Data Science platform for more information.

Data Scientist vs Machine Learning Engineer: Salary

Now you know what kinds of skills and education you should develop for these roles, let’s take a look at what kind of compensation you can expect, comparing data science vs machine learning engineering. This table shows what percentage of job postings mentioned a certain salary range.

• Higher salary ceiling for ML Engineers: A larger percentage of ML engineer positions offer salaries above \$200,000 compared to data scientist roles. This suggests that top-tier ML engineers have the potential to earn more.
• Similar mid-range opportunities: Both roles have comparable percentages in the \$100,000 - \$160,000 range, indicating similar opportunities for mid-level professionals in both fields.
• More entry-level positions for Data Scientists: Data scientist roles show higher percentages in lower salary ranges (below \$100,000), suggesting more entry-level or junior positions available in this field.

It's important to note that these percentages represent only the job postings that mentioned salaries, which is a minority of all postings. Factors such as experience, location, and specific company policies can also significantly influence individual salaries.

To corroborate our findings, let's examine data from Glassdoor. Their statistics reveal that in 2024, the average annual salary for data scientists in the US is approximately \$160,000, while machine learning engineers earn slightly more at \$166,000, agreeing with our research.

Data Scientist vs Machine Learning Engineer: Career Path

Both data scientists and machine learning engineers have exciting career paths, with opportunities for growth and specialization. Here's a comparative look at their potential career trajectories:

Data Scientist Career Path

• Entry-level: Junior Data Analyst or Data Scientist
• Mid-level: Senior Data Scientist or Data Science Team Lead
• Advanced: Principal Data Scientist, Head of Data Science, or Chief Data Officer
• Specializations: Business Intelligence Analyst, Quantitative Analyst, or Data Engineer

Machine Learning Engineer Career Path

• Entry-level: Junior ML Engineer or AI Developer
• Mid-level: Senior ML Engineer or ML Team Lead
• Advanced: Principal ML Engineer, Head of AI, or Chief AI Officer
• Specializations: Computer Vision Engineer, NLP Specialist, or MLOps Engineer

Aligning with our comparison of data scientist vs machine learning engineer job postings above, data scientists have more opportunities to transition into more business-oriented roles, while ML engineers often progress deeper into technical specializations.

Both careers offer the potential to move into leadership positions, start-ups, or consultancy roles. The key to advancement in either field is continuous learning, as the landscape of AI and data science is constantly evolving.

Data Scientist vs Machine Learning Engineer: Key Differences

Here is a summary of the key differences between these roles discussed throughout this article.

How to Become a Data Scientist

Becoming a data scientist requires a combination of education, skills, and practical experience. Here's a roadmap to help you get started:

• Develop a strong foundation: Focus on mathematics, statistics, and programming. Our Mathematics and Statistics courses provide essential knowledge.
• Learn programming languages: Master Python and R. Check out our comprehensive Python Programmer Bootcamp.
• Gain data analysis skills: Learn data cleaning, visualization, and exploratory data analysis. Our Data Preprocessing with pandas and Data Visualization courses cover these crucial skills.
• Study machine learning: Understand key ML algorithms and their applications with our Machine Learning course.
• Practice with real-world projects & build your portfolio: Apply your skills to practical problems. Explore our projects for hands-on experience.
• Network and stay updated: Join data science communities and attend conferences. Follow our Blog for industry insights and trends.

Ready to start your journey? Our Data Scientist Career Track offers a structured path with all the resources you need to become a successful data scientist in 2024.

How to Become a Machine Learning Engineer

Becoming a machine learning engineer requires a strong technical background and specialized skills. Here's a roadmap to help you get started:

• Build a foundation in computer science: Focus on algorithms, data structures, and software engineering principles.
• Master programming languages: Become proficient in Python and Java. Our Python Programmer Bootcamp covers essential Python skills.
• Learn machine learning algorithms: Understand both traditional and deep learning algorithms. Explore our comprehensive Machine Learning course.
• Gain experience with ML frameworks: Learn TensorFlow, PyTorch, and scikit-learn. Our courses cover these popular frameworks in depth.
• Develop skills in data processing and analysis: Learn to work with large datasets efficiently. Our Data Preprocessing with pandas course introduces a crucial tool.
• Study MLOps and deployment: Understand how to deploy and maintain ML models in production environments.
• Work on practical projects: Apply your skills to real-world problems. Check out our projects for hands-on experience in building ML systems.
• Stay updated with the latest trends: Follow our Blog for the latest developments in machine learning and AI.

Ready to embark on your ML engineering journey? We're excited to announce our new Machine Learning Engineer Career Guide for 2024. This comprehensive guide offers a structured path with all the resources you need to become a successful ML engineer in today's competitive landscape.

While both data scientists and machine learning engineers play crucial roles in the world of AI and data, understanding the distinctions between a data scientist vs machine learning engineer is key for anyone looking to enter or advance in these fields, helping you choose the path that best aligns with your skills, interests, and career goals.

FAQs