Data Scientist Vs ML Engineer: What Are The Differences?
Updated 5 June 2023
Data science is a fast-growing field in which individuals use a range of techniques and tools to generate useful insights from large volumes of data. Machine learning (ML) is a subset of artificial intelligence that enables computer systems to automatically improve their performance on a specific task or a range of tasks over time. If you are interested in pursuing a career in either of these domains, you may benefit from learning about the differences between them. In this article, we examine some crucial differences between the roles of a data scientist vs ML engineer.
What Is A Data Scientist?
A data scientist is an individual who uses their expertise in mathematics, statistics and computer science to extract insights and knowledge from large, complex datasets. They work with both structured and unstructured data, using a range of analytical techniques and tools to identify patterns, develop models and make predictions. Data scientists are also responsible for cleaning and organising data and communicating their findings to employers and the public in a clear and understandable manner.
What Is An ML Engineer?
An ML engineer is someone who specialises in developing, deploying and maintaining machine learning models in various types of systems. They work closely with data scientists and software engineers to implement models that seamlessly integrate into software applications, data pipelines and other systems. ML engineers are responsible for ensuring that models satisfy performance, scalability and reliability parameters, and that they are able to function effectively in real-world environments. This involves selecting appropriate algorithms and tools, building custom software solutions and using techniques such as data pre-processing, feature engineering and model evaluation.
Data Scientist Vs ML Engineer
While comparing a data scientist vs ML engineer role, it is beneficial to consider these parameters:
These are some typical duties of data scientists:
Collecting and cleaning large, complex datasets from a variety of sources
Analysing and visualising data to identify patterns, trends and insights
Developing statistical and machine learning models to make predictions and drive decision-making
Sharing insights and findings to a larger audience while ensuring understandability
Collaborating with cross-functional teams to develop data-driven solutions to business problems
Validating models through the careful design of experiments
Developing and implementing data quality checks to ensure accuracy and consistency
These are some typical duties of ML engineers:
Collaborating with data scientists and software engineers to design and develop machine learning models that integrate well into production systems
Selecting appropriate algorithms and techniques to optimise model performance, scalability and reliability metrics
Developing custom software solutions to support machine learning workflows and data pipelines
Handling data pre-processing and feature engineering workflows to improve the accuracy and effectiveness of machine learning models
Developing and implementing model evaluation strategies to measure performance and identify areas for improvement
Designing and conducting experiments to test and validate machine learning models
Developing and maintaining monitoring and maintenance strategies to ensure the ongoing performance and reliability of machine learning models in production systems
A data scientist typically has a strong academic foundation in mathematics, statistics and computer science, which they often develop through formal education. Most employers require candidates to hold at least a bachelor's degree in a related field, such as computer science, mathematics, statistics or data science. Many data scientists hold advanced degrees, such as a master's degree or doctorate in data science or a related field, which can provide more specialised training and research experience. Formal education in data science typically includes coursework in calculus, linear algebra, statistical inference, experimental design, data structures and programming languages.
An ML engineer typically has a relevant academic foundation in computer science, mathematics and software engineering. A bachelor's degree in a related field, such as computer science or software engineering, is ideal, and many employers prefer hiring candidates with advanced degrees in computer science or a related field with a focus on machine learning. In addition to formal education, ML engineers typically possess experience working with data and coding in a variety of languages. They may also have experience working with machine learning frameworks and software engineering practices, such as version control, software design patterns and continuous integration and delivery.
These are some crucial skills for data scientists:
Mathematical and statistical skills: Data scientists usually have a strong foundational understanding of mathematical and statistical concepts to understand, analyse and manipulate data effectively.
Coding and data manipulation: Data scientists are typically proficient in coding and data manipulation techniques to process and analyse large datasets efficiently.
Data visualisation and reporting: This role may require you to present complex data insights in a visually compelling and easily understandable way to interested parties
Machine learning algorithms: These individuals benefit from developing a good understanding of various machine learning algorithms to develop and apply models that can learn from data and make accurate predictions.
Communication skills: Data scientists are often responsible for communicating complex insights and technical concepts to non-technical collaborators in a clear and concise manner.
Problem-solving and critical thinking: Data scientists require the ability to approach business problems in a structured and logical way to develop effective solutions.
Collaboration skills: The data scientist role involves working effectively in cross-functional teams and communicating technical concepts to non-technical collaborators.
Big data processing: This role requires individuals to be familiar with big data processing technologies.
Project management skills: In this role, it is critical to be able to prioritise tasks, manage timelines and deliver high-quality results on time.
Continuous learning: Data scientists who have a passion for learning and staying up to date with the latest technologies and techniques in their field are often successful in their careers, as theirs is a fast-evolving domain.
These are some crucial skills for ML engineers:
Programming: ML engineers who have a strong foundation in programming languages may be able to develop efficient and scalable machine learning algorithms and systems.
Machine learning frameworks: ML engineers who have proficiency in machine learning frameworks may be able to design, implement and optimise machine learning models effectively.
Cloud platforms: As an ML engineer, you benefit from being familiar with cloud-based platforms to deploy, scale and manage machine learning models in a variety of business environments.
Software engineering best practices: ML engineers typically follow best practices in software engineering to ensure that machine learning code is maintainable, reliable and reusable by virtue of its modularity.
Data engineering concepts: In this role, you benefit from being familiar with data engineering concepts to create efficient data pipelines to feed data to machine learning models.
Big data processing technologies: ML engineers are typically familiar with big data processing technologies that enable them to manage and analyse large volumes of data efficiently.
Problem-solving and critical thinking: Engineers in this domain solve complex business problems using tried and tested techniques in a structured and logical way.
The work environment of a data scientist is typically collaborative, analytical and data-driven. Data scientists work in cross-functional teams with data engineers, ML engineers and product managers to solve business problems using data. They may spend a significant amount of time analysing data, exploring patterns and developing insights to inform business decisions. Their work environment can be dynamic and fast-paced and they may require flexibility and adaptability to new tools and technologies as the field evolves. Data scientists typically work efficiently and effectively to deliver high-quality results in a timely manner.
The work environment of an ML engineer is often fast-paced, dynamic and highly technical. ML engineers work closely with data scientists and data engineers to develop, deploy and maintain machine learning models for a variety of business applications. These individuals often work on multiple projects simultaneously and quickly adapt to changing business needs and technical requirements. Their work can be challenging but rewarding, as they play a critical role in developing intelligent systems that can help private- and public-sector organisations make data-driven decisions and improve outcomes that may potentially affect numerous individuals.
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