Artificial Intelligence Engineer or Data Scientist? Make the Right Choice
An AI engineer is responsible for developing apps and products for autonomy; the functioning of a data scientist revolves around data products.
AI and Data Science have become remarkable career choices in the current job market. While an AI engineer uses machine learning techniques to build models to upscale AI applications, a data scientist gathers, analyses, and transforms data. These careers sound the same but have different job responsibilities and require different skill sets.
Let us learn more about these two professions and understand their underlying differences.
AI Engineer and Data Scientist
Artificial intelligence and data science are the tech fields that offer multiple job opportunities. These distinct career fields have immense scope. Knowing the differences between these two is important to choose the right career option that aligns with your interests and goals. Let us start knowing about them individually.
AI Engineer
AI engineers solve complicated business problems, automate tasks, improve decision-making, support clients, create new products, and enhance business operations’ efficiency. AI engineer certification teaches you to work on algorithms that can replicate human capabilities. These algorithms can solve a range of problems using massive data. AI creates hardware or software models to perform desired tasks like image recognition, answering customers, decoding human speech, translating, etc. Artificial intelligence consists of two major categories. Applied AI also uses sensory technologies for different activities.
Data Scientist
Data scientists use database technologies, including big data analytics, cloud computing, etc., to collect, evaluate, and assess datasets and extract meaningful information for future use. They use various strategic and mathematical tools to extract valuable insights from large and unstructured data sets. They help companies make informed business based on customer and business data. They are adept in data collection, organization, visualization, and maintenance.
While all large and medium businesses need data scientists to help businesses make correct decisions, data science cannot work without AI. Data science uses AI to generate a successful business.
Artificial Intelligence Engineer v/s Data Scientist: The Key Differences
Here, we jot down the main differences between a data scientist and an artificial intelligence engineer:
➥ Job Role and Responsibilities
A data scientist is mostly engrossed in data. They gather the data, analyze it, and derive relevant information from it. They also identify data science problems, find the source, and asses the data to arrive at a solution. They ensure to provide a comprehensive solution after using an array of multiple high-tech tools and data science technologies. They are also proficient with machine learning, statistical modeling, data mining, data analytics research, etc.
An artificial intelligence engineer develops AI products. They also use big data and the data science team’s help to offer augmented efficiency and an improved business organization. They design and build tech solutions to use machine learning and deep learning. They develop scalable algorithms using object tracking and detection, semantics, instance segmentation, etc.
➥ Working Techniques
Data scientists primarily use statistical and mathematical methodologies in their work. They analyse the data and conclude the usable data. Artificial intelligence engineers use machine learning, automation, robotics, natural language processing, etc., to implement apps and models.
➥ Tools
Data scientists use tools and programming languages like Tableau, Knime, Spark, Hive, Pig, Rapid Miner, Python, R, Apache, Hadoop, etc., to complete the desired task. Artificial intelligence engineers use tools like TensorFlow, PyTorch, Caffe, Scikit-Learn, etc. to build models.
➥ Skills Needed
These two job roles need two separate skill sets. A Data Scientist would need technical skills like mathematics and statistics, programming languages proficiency, data visualization tools, data cleaning, data mining, data management, database knowledge including SQL and other relational databases, etc. Besides technical skills, data scientists must also remain attentive to details, have good verbal and written communication skills, teamwork and collaboration skills, patience and persistence, and leadership skills.
AI engineer needs a good understanding of computer science, software engineering, algorithms, proficiency in programming, Unix and Linux, data evaluation techniques, etc. They should work on deep learning, machine learning, NLP, neural network architectures, etc. AI engineers must also hone soft skills like developing critical thinking, problem-solving, creativity, networking, communication, etc.
➥ Scope
The scope of artificial intelligence is expanding. Today, more and more industries are using AI technology, pushing the demand for AI careers. There is immense scope in the field of data science, too. Companies hire data scientists to improve their business operations. Those who want to work with data technology can start their career in data science.
Wrapping up
You can decide which career option suits you based on the above-mentioned parameters. Ensure to improve your technical skill and soft skills. Learn from successful people in the same field and stay updated with the latest data science and artificial intelligence.
