Data Scientists vs AI Engineers: What's Your Pick in 2024?

Data Scientists extract valuable insights and knowledge from structured and unstructured data using techniques from statistics, scientific computing, and algorithms. They use their domain expertise and analytical skills to analyze large datasets, develop predictive models, and provide data-driven recommendations.

Data Scientists are required in various fields, such as business, healthcare, finance, etc., by processing and interpreting data to uncover hidden patterns and trends. They use advanced analytics techniques, like machine learning, to derive meaningful information, aiding organizations in making informed decisions and solving complex problems using data-driven strategies. The next advanced role that one takes after this is Senior Data Scientist.

AI Engineers specialize in developing algorithms and technologies that mimic human intelligence. They create intelligent systems that can learn, analyze data, make predictions, and perform tasks with minimal human intervention. AI Engineers use machine learning, deep learning, and neural networks to build models that can recognize patterns, solve complex problems, and improve decision-making processes. 

They design, train, and fine-tune Artificially Intelligent models to achieve goals like natural language processing, computer vision, and predictive analytics. AI Engineers are needed in various fields, such as speech recognition, image processing, robotics, and more, and their expertise is crucial in shaping the future of technology.

To become an AI Engineer or a Data Scientist, you must have certain educational qualifications. There are certain differences in the educational qualifications for both roles, and you can find the required qualifications below: 

AI Engineers must have some of the following qualifications and skills mentioned below:

• A bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
• Proficiency in programming languages like Python, C++, Java, etc.
• Strong knowledge of mathematics, including statistics, linear algebra, calculus, etc.
• Familiarity with Machine Learning concepts, data preprocessing, cleaning, etc. 

To make a career as a Data Scientist, you must have the following qualifications and skills mentioned below:

• A bachelor’s degree in Computer Statistics, Statistics, Engineering, or a related field. 
• Proficiency in programming languages like R and Python for data cleaning, preprocessing, and model development.
• Strong background knowledge of Probability and Statistics. 
• Familiarity with machine learning algorithms and techniques. Knowledge of data visualization libraries and tools like Matplotlib, Seaborn, etc., is also required.

AI Engineers and Data Scientists have similar responsibilities. They both utilize Machine Learning algorithms to extract valuable insights and create AI-powered applications. Here are some responsibilities that set them apart:

Data Scientist

• Collect and preprocess data from various sources.
• Analyze and interpret large datasets to identify patterns and trends.
• Develop predictive models and machine-learning algorithms.
• Utilize data visualization techniques to present information effectively.
• Automate data collection and processing tasks.
• Collaborate with cross-functional teams, including engineering and product development.
• Identify valuable data sources and design automated collection processes.

AI Engineer

• Develop and program intelligent algorithms for AI applications.
• Utilize AI and machine learning techniques to create systems and applications.
• Build, test, and deploy AI models using deep learning, neural networks, and ML algorithms.
• Design AI models for deriving business insights and supporting informed decision-making.
• Process and analyze large datasets using various tools and methods.
• Implement AI systems for various applications like language translation, visual recognition, and sentiment analysis.
• Collaborate with cross-functional teams to integrate AI capabilities into products.

AI Engineers and Data Scientists have lucrative salaries, which is crucial in deciding the career field. Here is the earning potential of both roles:

Data Scientist

• The average annual salary of a Data Scientist in the US is around $50,734.
• The average annual salary of a Data Scientist in India is around ₹4.223,618.
• The average annual salary of a Data Scientist in the UK is around £41,602.

AI Engineer

• The average annual salary of an AI Engineer in the US is around $70,488.
• The average annual salary of an AI Engineer in India is around ₹5,868,126.
• The average annual salary of an AI Engineer in the UK is around £57,800.

The salary data is dynamic and is subject to change based on different factors.

You must have various technical skills to enter the Data Science or AI Engineering sector. Here is a list of technical skills you should master to make a successful career as a Data Scientist or AI Engineer:

Data Scientist

You can become a Data Scientist with the necessary Data Science skills. Here are some of them mentioned below:

• Programming languages: Data Scientists must be proficient in programming languages like Python and R to manipulate and analyze large datasets.
• Probability and Statistics: Mathematics is an important Data Science skill with various branches like Probability and Statistics. It is important to have a solid understanding of probability and statistics to extract valuable insights and patterns from complex datasets.
• Data Wrangling and Database Management: Data Scientists must be proficient in data wrangling and database management to clean, organize, and transform complex data sets.
• Data Visualization: Data visualization is an important skill for Data Scientists as they interpret data trends and patterns through charts, graphs, and dashboards.
• Machine Learning: Data Scientists must have a strong understanding of Machine Learning algorithms and techniques to build predictive models and analyze data patterns.

AI Engineer

You can become an AI Engineer if you have the necessary skills. Here are some of them mentioned below:

• Programming Languages: Data Scientists must be proficient in programming languages like Python, Java, and C++ to build and optimize AI models. Languages like Python are recession-proof skills and must be learned to secure a career.
• Mathematics, Linear Algebra, and Statistics: A strong foundation in Mathematics, Linear Algebra, and Statistics is important to understanding and developing advanced AI algorithms.
• Machine Vision and Natural Language Processing (NLP): Familiarity with Machine Vision and Natural Language Processing (NLP) is important to understanding and interpreting visual information and human language.
• AI Platforms and Tools: Data Scientists must be proficient in AI Platforms and Tools like Google's Vertex AI, Amazon Web Services, and IBM Watson Studio to design, develop, and deploy AI solutions.
• Software Development: Strong understanding of software development is important for creating AI applications and systems.

In the ever-evolving landscape of technology, choosing between a career as a Data Scientist or an Artificial Intelligence Engineer depends on one's skills and interests. Both roles hold immense significance, driven by the growing demand for AI and data science professionals. 

As technology continues to evolve, both these professions will remain vital pillars of the tech world, offering diverse opportunities for those passionate about shaping the future through data and AI innovations. To become a Data Science or AI Engineer, you can enroll in various AI and Data Science courses listed at Careervira.