Everything you need to know: AI Engineer vs Data Scientist

The technology sphere, which is constantly changing, has brought forth two new and vigorous domains of study: artificial intelligence (AI) and data science. Due to the rapid development of AI and data-driven intelligence, the number of jobs required in the domains is rapidly growing, too.

AI engineering includes designing, testing, and maintaining operating artificial intelligence systems. In contrast, data science is a cross-disciplinary field that stems from an understanding of insights from the provided data.

The rapid digitalization of business and the advent of a large volume of data show that the skills needed to create artificial intelligence systems and extract value from data are in high demand. But what is the difference between the two fields? Let’s find out. This blog delves into the distinctions between AI engineers and data scientists.

Must read: Everything about the future of AI and Data Science

Understanding AI Engineering

AI (artificial intelligence) engineering is a domain that differs from regular engineering in focus (designing, developing, and deploying AI systems). Its key goal is to develop computer systems and devices possessing features that normal human intelligence uses, like sensing, reasoning, perceiving, and learning.

Objectives of AI Engineering:

• Generate and optimize algorithms for machine learning applications to run.
• Bolster AI applications and systems by incorporating AI technologies, for instance, linguistic processing, computer vision, or speech recognition.

Key Responsibilities and Tasks of AI Engineers:

• Collaborate with stakeholders to understand business problems and define AI solutions.
• Analyze and explore datasets to identify patterns and trends that inform the development of AI models.

Understanding Data Science

Data science is a multidisciplinary subject that blends techniques from statistics, mathematics, computer science, and domain expertise to facilitate the discovery of significant knowledge and useful information from huge masses of data.

Objectives of Data Science:

• Gain insights into the structure and characteristics of the available data.
• Identify patterns and trends within the data that may provide valuable information.

Key Responsibilities and Tasks of Data Scientists:

• Collect data from several platforms, including databases, APIs, and external datasets.
• Clean and pre-process raw data to ensure accuracy and consistency.
• Explore and visualize data to identify patterns, outliers, and relationships.

Must read: How Does an MSc in Data Science Help You in the AI Era?

AI Engineer vs. Data Scientist: Key Differences

1. Focus and Objectives

AI Engineer

Focus: Building and deploying AI systems and applications.

Objectives: Introduce complex mechanisms such as intelligent systems, machine learning models, and applications that can perform tasks without being controlled or guided by humans.

Data Scientist

Focus: Having the information, data can be scanned and insights extracted to make important decisions in the organizational setting.

Objectives: Unveil patterns, trends, and saleable data in large data sets.

Quick read: Why you should become a data scientist?

2. Skillsets

Skill sets unique to AI engineers and data scientists:

AI Engineer

• Strong background in software engineering, machine learning, and AI technologies
• Proficiency in various programming languages, for instance, Python, Java, or C++
• Experience with high-level deep learning frameworks like Python or TensorFlow

Data Scientist

• Skill in functioning with or making statistics, data analysis, and machine learning algorithms
• Skills such as Python or R programming environments
• Knowledge of pre-processing data, doing EDA, and building predictive models

3. Tools and Technologies

Technologies and tools commonly used in AI Engineering projects:

Programming Languages	PythonJavaC++
Machine Learning Libraries	TensorFlowPyTorchScikit-Learn
Cloud Platforms	AWSAzureGoogle Cloud Platform

Technologies and methodologies commonly used in Data Science projects: 

Programming Languages	PythonR
Data Analysis and Visualization	PandasNumPyMatplotlibSeaborn
Machine Learning Libraries	Scikit-LearnTensorFlowPyTorch

4. End Goals

AI Engineer: Make artificial intelligence systems capable of doing specific functions without explicit programming.

Data Scientist: Instill effective and responsible data governance practices to maintain performance, continuity, and accountability in the business.

Career paths in AI Engineering and Data Science

1. Job Roles and Responsibilities in AI Engineering and Data Science

AI Engineering

Roles:

• Machine Learning Engineer
• AI Research Scientist
• Computer Vision Engineer

Responsibilities:

• Building and deploying machine learning models.
• Developing AI algorithms and applications.

Data Scientist

Roles:

• Data Analyst
• Data Scientist
• Business Intelligence Analyst

Responsibilities:

• Interpreting and dealing with multi-faceted datasets.
• Build valuable predictive models for business insights.

2. Educational Background and Skills

AI Engineer

Educational Background: A degree in computer science, mathematics, engineering, or any other related field.

Skills:

• A co-op employment experience that culminated in strong programming skills (Python, Java, C++).
• Algorithmic understanding of machine learning tools and frameworks.

Data Scientist

Educational Background: A degree in statistics, mathematics, computer science, or science-related fields.

Skills:

• In-depth data research and problem-solving.
• Programming abilities such as Python or R.

3. Industry Applications and Opportunities

Industry trends and demand for AI Engineers and Data Scientists:

Role	Industry Applications	Opportunities
AI Engineer	Healthcare: AI for medical image analysis and diagnostics.Finance: Fraud detection and algorithmic trading.	Research and development roles in tech companies.Implementation of AI solutions in various industries.
Data Scientist	Retail: Customer segmentation and demand forecasting.Healthcare: Predictive analytics for improved patient outcomes.	In-house data science teams in various industries.Consulting roles and providing data-driven solutions.

Challenges and Future Outlook

Challenges faced by AI engineers and data scientists:

• Addressing ethical considerations in AI, including bias in algorithms and the responsible use of data.
• Facilitating effective collaboration between technical experts, domain specialists, and business stakeholders.

Emerging trends and advancements:

• Explainable AI (XAI) is increasing the need for the development of AI systems that are more transparent and interpretable by humans to generate trust among people.
• Advancements in deploying AI models on edge devices enable faster and more efficient processing.

MAHE is the best choice for both: AI Engineer or Data Scientist!

Manipal Academy of Higher Education (MAHE) helps youths become experts in artificial intelligence (AI) and data science. MAHE offers the latest syllabi in these fields. The quality of the institution’s top-notch labs and research centers is a crucial factor in shaping students’ learning experiences.

The curriculum is intended to be complete, thus helping the graduates acquire skills and understanding of AI and data science models. It offers an MSc in Data Science, an MSc in Biostatistics, a BTech in Computer Science, a BTech in Artificial Intelligence and Machine Learning, etc.

Conclusion

To sum up, AI engineers and data scientists have similar starting points, with a foundation in data and technology. However, their different job functions make them compulsory professionals in the rapidly changing world of AI and data science. AI engineers create and utilize smart systems focused on solutions and user interaction. In contrast, data scientists concentrate on using data to inform decision-making and business strategies, gathering correct insights from using data. Both occupations offer different ways of careers, and the demand for AI engineers and data scientists is increasing with the development of new technology. Therefore, it plays a vital part in the future development of data-driven innovations in different industries.