Data Scientist vs. ML Engineer vs. AI Engineer: Who Should You Hire?
By
Samantha Cox
•
Jun 11, 2025
Torn between becoming a data scientist or a machine learning engineer? You’re not alone, and the difference isn’t always obvious. In this article, we’ll break down what each role involves, how their responsibilities and skill sets compare, and what their career paths look like. By the end, you’ll have a clearer picture of which path aligns best with your goals and which one might be your perfect fit.
Key Takeaways
Data scientists focus on deriving insights from data to influence business decisions, while machine learning engineers concentrate on developing, deploying, and optimizing machine learning models.
Both roles typically require a strong educational background in fields like computer science or mathematics, with continuous learning being essential for career advancement.
Understanding the distinct responsibilities and skills required for data scientists and ML engineers is crucial for making informed hiring decisions.
Understanding the Roles
To hire effectively, understanding the distinct roles of data scientists and machine learning engineers is crucial. While both leverage data to address business challenges, their approaches and focuses differ significantly, especially in the context of engineer vs data scientist.
Data scientists develop solutions for business challenges using machine learning or deep learning models. They analyze data, apply statistical methods, and influence decision-making processes through data mining. Data scientists work to create effective strategies.
Machine learning engineers are the ones who bring AI models to life. They focus on building, fine-tuning, and deploying those models so they run smoothly and efficiently in real-world systems. Compared to other roles in the AI space, ML engineers are more hands-on with the technical side, making sure everything works at scale and performs well. Their work is essential for turning cutting-edge ideas into real, working solutions that drive innovation forward.
What Does a Data Scientist Do?
Data scientists are the detectives of the data world. Their primary responsibilities include:
Analyzing large datasets
Extracting insights
Identify patterns
Forecasting outcomes
Evaluating trends
Analyzing user metrics
Forecasting risks
This role is critical in providing actionable insights and extracting valuable insights to predict future outcomes derived from complex data, influencing key business decisions.
Data scientists utilize a broad range of technical skills to perform their duties effectively. They are proficient in programming languages such as:
R
SQL
These languages are essential for:
Data analysis
Data manipulation
Data visualization
These skills allow them to:
Clean and preprocess data
Conduct exploratory data analysis
Create visualizations that effectively communicate their findings
Data scientists often collaborate with data engineers and analysts to gather and preprocess data for analysis. Teamwork ensures data is ready for processing and analysis, leading to accurate and reliable insights. Moreover, they employ technologies such as big data analytics and cloud computing to enhance their data processing capabilities.
Continuous learning and staying updated with the latest tools and technologies are vital in the dynamic field of data science. Many data scientists pursue advanced degrees and certifications to stay competitive and enhance their skills. This commitment to learning ensures that they are well-equipped to handle the ever-evolving challenges of data analysis and predictive analytics.
Data scientists extract statistical knowledge from data to solve business problems, making them invaluable assets. Their analytical and technical expertise makes them crucial for organizations leveraging data for strategic decisions.
What Does a Machine Learning Engineer Do?
Machine learning engineers and software engineers build intelligent systems. They create machine learning models that learn from data and make predictions. Their work involves creating algorithms and enhancing their performance in production environments. Refining predictive models requires a deep understanding of machine learning techniques and computational power.
A core focus for machine learning engineers is predictive modeling, involving predictions about future events based on past data. They deploy ML and deep learning models into production, ensuring effective and efficient functionality. This includes optimizing models for model performance, latency, and resource usage.
Machine learning engineers and data scientists collaborate with software developers to build and implement models. Such collaboration ensures models are accurate, scalable, and robust enough for real-world data. They also need skills to build data pipelines and train models based on experiments conducted by data scientists.
Machine learning engineers and AI engineers use advanced techniques like neural networks, deep learning, and natural language processing to create AI engineering applications. These machine learning systems enable the development of models for complex tasks, from image recognition to natural language understanding, including reinforcement learning and machine learning algorithms.
Machine learning engineers bring machine learning models to life through training models. Their expertise in model deployment and optimization is essential for organizations leveraging AI and machine learning for analytics and decision-making.
Educational Backgrounds
Educational backgrounds significantly shape the capabilities of data scientists and machine learning engineers. Both typically require at least a bachelor’s degree in computer science, mathematics, or data science. Continuous learning and staying updated with industry trends are crucial to remain competitive in these fields.
Academic Requirements
Employers expect data scientists to have at least a bachelor’s degree in computer science, data science, data analytics, or a related field. Common degrees for data scientists include computer science, mathematics, and statistics, with a strong foundation in these areas being essential. Advanced degrees, such as a master’s, enhance competitiveness for data scientists, making them attractive candidates for senior roles.
Machine learning engineers are generally expected to hold at least a bachelor’s degree in computer science, information technology, software engineering, math, or statistics. Educational backgrounds for machine learning engineers commonly include degrees in engineering, computer science, and machine learning. Like data scientists, advanced degrees can significantly boost career prospects and earning potential for machine learning engineers.
Continuous Learning
Continuous learning is crucial in the tech industry to remain competitive. Many professionals stay updated with industry trends through online courses and certifications. These resources offer valuable opportunities for data scientists and machine learning engineers to enhance skills and knowledge, ensuring they can tackle new challenges and technologies using training data.
Professionals use online courses and certifications to keep skills sharp and stay abreast of advancements in machine learning models, algorithms, and techniques. Ongoing commitment to learning is vital for career growth and success in data science and machine learning engineering.
Technical Skills
Technical skills critically differentiate data scientists from machine learning engineers, highlighting the key differences between the two roles. Both roles require a strong foundation in programming and data analysis, but their specialized skills and focuses vary significantly.
Understanding these technical requirements helps identify a deeper understanding of the right candidate for business needs.
Core Programming Skills
Data scientists use a range of programming languages including:
Python
Java
C++
R
Scala
Python and R are emphasized in training programs for their versatility and powerful libraries for data analysis and visualization. Proficiency in SQL is essential for effective data manipulation and querying large datasets.
Machine learning engineers primarily require programming skills in Java and C++. Python is essential for machine learning engineers due to its extensive libraries and frameworks for machine learning and deep learning. Proficiency in these languages enables ML engineers to build, train, and deploy machine learning models effectively.
Specialized Skills
Creating data visualizations to communicate analytical insights and visualize data is a key responsibility of data scientists. They design experiments to validate hypotheses and assess the success of implemented solutions through hypothesis testing. This requires a strong foundation in statistical methods and data analysis tools.
Machine learning engineers develop infrastructure that supports machine learning workflows. This includes:
Designing and implementing distributed systems for large-scale machine learning tasks.
Managing containerization in cloud platforms using tools like Docker and Kubernetes.
Having a strong understanding of linear algebra, probability, and statistics.
Overlapping Skills
Data scientists and ML engineers share common skills such as machine learning fundamentals, data manipulation, and problem-solving. Strong problem-solving abilities are essential for effective data analysis and model development in both roles. These shared skills facilitate collaboration and ensure effective contributions to the data science team.
Employers increasingly prioritize practical skills over formal degrees, emphasizing hands-on experience as critical for both data scientists and ML engineers. This approach ensures candidates can effectively apply their knowledge to solve real-world business problems and drive innovation.
Roles and Responsibilities
Understanding the distinct roles and responsibilities of data scientists and machine learning engineers is crucial for hiring decisions. Data scientists focus on extracting insights from data and solving business problems, while ML engineers concentrate on designing, developing, and deploying machine learning models.
Data Scientist Responsibilities
Data scientists analyze data, apply machine learning principles in decisions, and communicate insights to stakeholders. Their tasks include data cleaning, exploratory data analysis, and creating visualizations to communicate findings. Statistical modeling is crucial for data scientists to derive insights and support decision-making.
Data scientists collect and analyze data to make recommendations, contributing to business processes. Their ability to provide actionable insights and support strategic decisions makes them invaluable for data-driven organizations.
ML Engineer Responsibilities
Machine learning engineers:
Design and implement new algorithms, as well as improve existing algorithms.
Build algorithms, perform testing, and consult with management to identify improvement opportunities.
Test machine learning models and algorithms using real-world data sets to ensure effectiveness.
Focus on advanced areas like deep learning and model deployment.
Ensure successful integration of models into production systems.
They focus on advanced areas like deep learning and model deployment, ensuring successful integration into production systems. As they advance, ML engineers may oversee teams and strategies related to machine learning deployment, with opportunities for managerial positions.
Salary Comparison
Salary comparisons are crucial when hiring top talent, impacting your ability to attract and retain skilled professionals. Understanding average salaries and factors influencing compensation helps make competitive offers.
Average Salaries
Recent reports indicate that data scientists earn approximately $120,000 per year, while ML engineers earn around $130,000 annually. In high-demand tech hubs like San Francisco and New York City, data scientists can earn up to $150,000, and ML engineers up to $160,000.
Remote work opportunities have expanded, with data scientists earning an average of $110,000 and ML engineers making about $120,000 remotely, slightly lower than in-office salaries. This work location flexibility can be an attractive benefit for many professionals.
Influencing Factors
Several factors influence the salary of data scientists and machine learning engineers, including:
Education level
Experience
Industry
Job location
Industry and job location affect salary levels, with certain sectors and regions offering more competitive compensation. Professionals in high-demand tech hubs like San Francisco and New York City tend to earn higher salaries compared to other regions. Understanding these factors helps offer competitive salaries to attract top talent.
Career Path and Growth Opportunities
Data scientists and machine learning engineers have exciting data science career paths. Their growth opportunities are also very promising. Individuals can progress from entry-level positions to senior leadership roles through structured career progression.
Continuous learning and staying updated with the latest industry trends are key to advancement in these fields.
Data Scientist Career Path
The career path for data scientists often begins with entry-level roles like Data Science Intern, eventually progressing to positions such as Senior or Lead Data Scientist, and potentially reaching executive roles like Chief Data Scientist. As they advance, their focus shifts from hands-on analysis to leadership, strategy, and high-level decision-making.
In these senior roles, data scientists use their expertise to guide business strategy, lead data teams, mentor junior colleagues, and spearhead innovative solutions to complex challenges.
ML Engineer Career Path
Machine learning engineers can start from entry-level positions and have the potential to transition to data scientists. A common career path for ML engineers is to progress deeper into technical specializations, such as deep learning, natural language processing, and computer vision.
As they advance, ML engineers may take on more complex tasks and responsibilities, including:
Overseeing teams and strategies related to machine learning deployment
Moving into senior roles, such as Senior ML Engineer or AI Architect
Eventually advancing into managerial positions
Implementing AI in Hiring Processes
Implementing AI in hiring processes can significantly streamline recruitment and enhance recruiter focus. AI-driven tools can automate repetitive tasks, allowing recruiters to concentrate on strategic activities and high-touch interactions with candidates.
Tools like Fonzi, a talent marketplace designed to hire top-tier AI and engineering talent, exemplify how AI can improve hiring efficiency without sacrificing human oversight.
Streamlining Recruitment
Automation through AI can efficiently perform candidate screening by:
Utilizing algorithms that analyze resumes and match qualifications accurately.
Screening large volumes of resumes to ensure that only the most qualified candidates are considered for interviews.
Employing AI-driven fraud detection mechanisms to identify and mitigate risks associated with false candidate identities during the hiring process.
AI-driven tools significantly enhance automation in recruitment processes, making tasks like candidate screening more efficient. This efficiency allows companies to reduce hiring cycles and improve the quality of their candidate pool, addressing some of the urgent hiring challenges faced by AI companies today.
Enhancing Recruiter Focus
AI is revolutionizing recruitment by automating repetitive tasks, allowing recruiters to focus on more strategic activities. Automation can take over tasks such as candidate screening and scheduling, freeing up recruiters to engage more deeply with candidates. By reducing the likelihood of human error in candidate evaluations, automation through AI increases the fairness and accuracy of the hiring process.
With AI handling routine tasks, recruiters can allocate more time to high-touch activities, such as personal interviews and relationship building, which are vital for achieving successful hires. This shift in focus can lead to better hiring outcomes and a more efficient recruitment process overall.
Summary
Choosing between a data scientist, a machine learning engineer, or an AI engineer depends on your specific business needs and goals. Data scientists excel at extracting insights from data and solving business problems, while machine learning engineers specialize in designing, developing, and deploying machine learning models. Both roles require a strong foundation in programming, data analysis, and continuous learning to stay competitive in the rapidly evolving tech industry.
By understanding the key differences, educational backgrounds, technical skills, responsibilities, and career paths of these roles, you can make informed hiring decisions that align with your company’s objectives. Additionally, leveraging AI in your recruitment process can streamline hiring, improve candidate quality, and allow recruiters to focus on strategic activities. Embrace the power of AI and data science to drive your business forward.
