Which Career is Better: A Data Scientist or an Artificial Intelligence Engineer
According to GlobeNewswire, the largest newswire distribution networks worldwide, the global artificial intelligence (AI) market is anticipated to grow from USD 20.67 billion in 2018 to USD 202.57 billion by 2026.
While the data science global market anticipates reaching more than USD 178 billion by 2025.
Both data science and AI have been touted to be remarkable careers in the tech industry.
Without wasting much time, let us delve deeper and talk more about data science and AI career.
Artificial Intelligence Engineer vs Data Scientist — A Broader Perspective
👉Artificial intelligence engineer
Artificial intelligence is no longer a thing of the past but instead has become a greater part of our everyday lives. From getting your groceries delivered to prompting Alexa to play your favorite song, AI is living within us.
An AI engineer with the help of machine learning techniques such as neural network helps build models to rev up AI-based applications. Some of the AI-based applications created by these engineers include language translation, visual identification, and contextual advertising based on sentiment analysis.
Organizations are now realizing the greatest impact AI and machine learning can cause on their business. Most of the business analytics professionals are upskilling and switching careers to become citizen data scientists. For an organization to become fully AI-driven, the organization must be able to implement AI into their applications. Doing this allows everyone within the organization to gain access to the insight for making better-informed decisions. Such organizations are now creating more artificial intelligence engineer positions for individuals capable of handling data science, software development, and hybrid data engineering tasks.
Artificial intelligence plays a crucial role in the life of a data scientist.
👉Data Scientist
Data scientists on the other hand use technologies like big data analytics, cloud computing, and machine learning to analyze datasets, extract valuable insights for future predictions. Simply said, data science cannot do without AI.
Both AI and data science have a distinctive role to play when it comes to generating a successful business. So, businesses need both AI and data science, if they’re looking to compete with jobs of the future.
Job Responsibilities Key Differences: Data Scientist vs AI Engineer
Although both have different job roles and responsibilities, it is best to say AI and data science work hand in hand. Both technologies have the potential to drive business to greater heights.
✏️Data Scientist
A day in the life of a data scientist mostly revolves around data. From gathering the data to analyzing the data and transforming the data, a data scientist might find themselves wrapped around these responsibilities.
- They need to possess skills to help identify a business or engineering-related problems and translate them into data science problems, find the sources, analyze the data that reveals useful insights to find a solution.
- Deliver end-to-end analytical solutions using multiple tools and technologies.
- In-depth hands-on experience working with machine learning, data mining, statistical modeling, and unstructured data analytics in research or corporate environment.
- Showcasing skills related to classification models, neural network, cluster analysis, Bayesian modeling, and stochastic modeling, etc.
✏️AI Engineer
An artificial intelligence engineer initiates, develops, and delivers production-ready AI products by collaborating with the data science team to the business for improved business processes.
- They are responsible for designing and building computer vision solutions to leverage machine learning and deep learning.
- Develop scalable algorithms by leveraging object tracking algorithms, instance segmentation, semantic, object detection, and keypoint detection.
- Use of machine learning methods like zero-shot, GANs, few-shot learning, and self-supervised techniques.
- Docker technologies to develop deployable versions of the model.
- Testing and deployment methods.
- Creating and deploying intelligent AI algorithms that function.
💲Who Earns Better: A Data Scientist or an AI Engineer
According to Payscale, the average salary of a data scientist ranges from USD 96k to USD 134k depending on the years of experience, level of expertise, and job location.
While an artificial intelligence engineer makes around USD 122,793 per year.
Major Key Skills Required: Data Scientist and an AI Engineer
✔️Data Scientist
- Mathematics and Statistics.
- Programming in R and Python.
- Extensive usage of big data tools — Spark, Hadoop, Hive, Pig.
- Data visualization tools — QlikView and Tableau.
- In-depth understanding of data cleaning, data management, and data mining.
- Database knowledge — SQL and other relational databases.
✔️AI Engineer
- Solid understating of computer science and software engineering.
- Mathematical and algorithms.
- Proficiency in R and Python programming.
- Great command over Unix and Linux environments.
- Data evaluation.
- Showcasing working experience in deep learning, machine learning, image processing, NLP, computer vision, and neural network architectures.
The tech industry is still facing challenges to recruit the best professionals in the field of data science and AI. While the job market is still booming, it is recommended for professionals to upgrade skills in both fields. One of the best ways to do it is by obtaining AI engineer certifications or data science certifications. You can choose any one of this job role that best fits your criteria.
📍Wrapping Up
The World Economic Forum predicts that by the end of 2020, we will have around 58 million newer jobs. Besides, at the beginning of 2020, AI specialists had been topped as one of the most sought after jobs in the AI field.
Not to mention, the world still needs to hire more data scientists to shrink the technology gaps.
What will you choose today: A data scientist or an AI engineer?
