IDC reported the global spending on AI technologies will hit $97.9 billion by the end of 2023. According to Gartner, 80% of merging technologies will have foundations in AI by the end of 2021. According to LinkedIn’s 2020 Emerging Jobs report, artificial intelligence engineers and data scientists continue to make a strong showing as the top emerging job roles for 2020 with 74% annual growth in the past 4 years. These statistics show that the growth in the implementation of AI solutions is fuelling demand for the skills needed to make them a success. Data science and artificial intelligence are the rocket ships that are taking off the post-pandemic era with lucrative pay-checks and rewarding perks. It’s no secret that data scientists and artificial intelligence engineers are crowned as the world’s fastest-growing and dynamic job roles at the moment that are crucial for the development of larger intelligence software products. Data scientist vs artificial intelligence engineer – two data job roles that are often used interchangeably due to their overlapping skillset, but are actually different.
A data scientist shouldn’t be confused with an artificial intelligence engineer. Though there is a huge overlap of skills, there is a difference between a data scientist and an artificial intelligence engineer, former is typically mathematical and literate in programming but they rely on highly skilled artificial intelligence engineers to implement their models and deploy them into the production environment. Both data scientists and artificial intelligence engineers are complementary job roles with overlapping skills that work well together in harmony and are equally important for the success of an AI project. Without much ado, let’s explore and understand the differences between – Data Scientist vs Artificial Intelligence Engineer.
Data Scientist vs Artificial Intelligence Engineer – A Breakdown
What is a Data Scientist?
A data scientist is a unicorn that utilises algorithms, math, statistics, design, engineering, communication, and management skills to derive meaningful and actionable insights from large amounts of data and create a positive business impact. Data scientists extensively use statistical methods, distributed architecture, visualisation tools, and diverse data-oriented technologies like Hadoop, Spark, Python, SQL, R to glean insights from data. The information extracted by data scientists is used to guide various business processes, analyse user metrics, predict potential business risks, assess market trends, and make better decisions to reach organisational goals.
What is an Artificial Intelligence Engineer?
From developing a robot hand for solving Rubik’s cube to speech recognition systems, artificial intelligence engineers are the one-man army imparting human intellect to machines. An artificial intelligence engineer is responsible for the production of intelligent autonomous models and embedding them into applications. AI engineers use machine learning, deep learning, principles of software engineering, algorithmic computations, neural networks, and NLP to build, maintain, and deploy end-to-end AI solutions. They work in collaboration with business stakeholders to build AI solutions that can help improve operations, service delivery, and product development for business profitability. Artificial intelligence engineers at some organisations are more research-focused and work on finding the right model for solving a task whilst training, monitoring, and deploying AI systems in production.AI engineers collaborate with business analysts, data scientists, and architects to ensure that business goals are aligning with the analytics back end.
Data Scientist vs Artificial Intelligence Engineer – Roles and Responsibilities
Let’s understand what does a data scientist and an artificial intelligence engineer do and what their job role entails.
What Does a Data Scientist Do?
Data scientists do everything right from setting up a server to presenting the insights to the board. Here are some core tasks a data scientist performs:
- Identify business problems and collect relevant, large datasets to solve these problems.
- Prepare, clean, transform, and explore data before analysis.
- Use state-of-the-art methods for data mining to generate new information.
- Choose and implement an appropriate machine learning family of algorithms for a business problem.
- Use various statistical modelling and machine learning techniques to measure and improve the outcome of a model.
- Tune and optimise model hyperparameters.
- Use various analytical methods and machine learning models to identify trends, patterns, and correlations in large datasets.
- Collaborate with data analysts, AI engineers, and other stakeholders to support better business decision making.
- Communicate the insights into various business stakeholders in a compelling way.
What Does an Artificial Intelligence Engineer Do?
- Create and deploy intelligent AI algorithms to function.
- Develop API’s that are scalable, flexible, and reliable to integrate data products and source into applications.
- Use Docker technologies to create deployable versions of the model.
- Build Infrastructure as Code – Ensure that the environments created during model development and training can be replicated with ease for the final AI-based solution.
- Develop and maintain architecture using leading AI frameworks.
- Use tools like GIT and TFS for continuous integration and versioning control to track model iterations and other code updates.
- Test and deploy models.
- Create any user interfaces required to display a more in-depth view of the models.
- Implement other software engineering concepts like continuous delivery, auto-scale, and application monitoring.
- Develop MVP applications that encapsulate everything right from model development to model testing.
- Look over the overall needs of the AI project.
Data Scientist vs Artificial Intelligence Engineer – Technical Skills
Artificial intelligence engineers have overlap with data scientists in terms of technical skills, For instance, both may be using Python or R programming languages to implement models and both need to have advanced math and statistics knowledge. However, AI engineers are expected to be more highly skilled when it comes to NLP, cognitive science, deep learning, and also have sound knowledge of production platforms like GCP, Amazon AWS, Microsoft Azure, and AI services offered by these platforms to deploy models in the production environment.
Skills Needed to Become a Data Scientist
- Strong math and statistics foundation.
- Programming in Python and R
- Know-how of big data tools like Hadoop, Spark, Pig, Hive, and others.
- Proficiency in using SQL and querying other relational database management systems.a
- Data visualisation tools like Tableau, QlikView, and others.
- Have a good understanding of data mining, data cleaning, and data management techniques.
Skills Needed to Become an Artificial Intelligence Engineer
- Proficiency in programming languages like Python and R.
- Fundamentals of Computer Science and Software Engineering
- Solid Mathematical and Algorithms Knowledge
- Data evaluation and architecture design.
- Good Command over Linux/Unix based commands as most of the processing in AI happens Linux-based machines.
- Knowledge of distributed computing as AI engineers work with large amounts of data that cannot be stored on a single machine.
- Cognitive Science to understand human reasoning, language, perception, emotions, and memory. Deeper insight into the human thought process is a must-have skill for AI engineers.
- Machine Learning, Deep learning, neural network architectures, image processing, computer vision, and NLP.
- Know-how of signal processing techniques for feature extraction.
Data Scientist vs Artificial Intelligence Engineer – Salary
Salaries for data scientists and artificial intelligence engineers are heading skyward and these vary based on skills, experience level, and the companies hiring. However, due to the increasing demand for skilled data scientists and artificial intelligence engineers, the salaries for these professionals are always burgeoning. According to PayScale, the average data scientist salary is 812, 855 lakhs per annum while artificial intelligence engineer salary is 1,500, 641 lakhs per annum. Based on the seniority level the salaries can go high as 30 lakhs per annum for a data scientist and 50 lakhs per annum for an artificial intelligence engineer.
Data Scientist vs Artificial Intelligence Engineer – In a Nutshell
|Data Scientist||Artificial Intelligence Engineer|
|Purpose||The primary goal of a data scientist is to uncover hidden trends and patterns present in the data. A data scientist works with structured and unstructured data by sourcing, cleaning, and processing it to extract valuable business insights.||The primary goal of an Artificial Intelligence Engineer is to bring autonomy to the models in production. An artificial intelligence engineer combines large amounts of data through intelligent algorithms and iterative processing to replicate human intelligence through machines.|
|Educational Requirement||Graduate degree in Computer science, Economics, Social sciences, Physical sciences, and Statistics. However, most data scientists have a Master’s or a Ph.D.||Graduate degree in Math, Statistics, Economics, Any engineering background, Computer Science, IT, Linguistics, or Cognitive Science.|
|Salary||812,855 INR||1,500,641 INR|
|Tools||Apache Hadoop, Apache Spark, Python, R, SAS, SPSS, Tableau, etc||Apache Mahout, Keras, TensorFlow, SciKit Learn, Shogun, Caffe, PyTorch,|
|Applications||Types of Data Products that a data scientist builds include – recommender systems, fraud detection systems, customised healthcare recommendations, and more.||Types of Applications that an artificial intelligence engineer builds include – Voice Assistants, Intelligent humanoid robots, Self-Driving Cars, Chatbots, and more.|
Data Scientist vs Artificial Intelligence Engineer – Complimentary Job Roles that are Better Together
An artificial intelligence engineer helps businesses build novel products that bring autonomy while a data scientist builds data products that foster profitable business decision making. AI engineers and data scientists work together closely to create usable products for clients. A data scientist builds machine learning models on IDE’s while an AI engineer builds a deployable version of the model built by data scientists and integrates these models with the end product. AI engineers are also responsible for building secure web service APIs for deploying models if required. In other words, a data scientist uses AI as a tool to help organisations solve problems while an artificial intelligence engineer productionises data science work to serve customers or internal stakeholders. Both data scientists and AI engineers keep themselves abreast of novel breakthrough tools and technologies that have the potential to transform consumer experience, business operations, and the workforce. However, a data scientist looks at the business from a higher strategic point than an artificial intelligence engineer. They both need to work collaboratively to build an AI solution that works with the best level of efficiency and accuracy when implemented in real-life.
So, should you become a data scientist or an artificial intelligence engineer?
According to the World Economic Forum, artificial intelligence will create 58 million new jobs by the end of 2020. Data scientists and artificial intelligence engineers are in the ascendancy, and it’s no surprise. New York Times reported that there are less than 10,000 qualified artificial intelligence engineers across the world, way too less compared to the demand reported. The industry is suffering from a huge skills gap for tech-based skillsets such as data analytics, data science, machine learning, and AI that continue to be in demand for 2020 and beyond.
Research by Livemint found that only 35% of AI professionals enter the industry with AI skills while 65% learn and add AI to the skills they have already acquired. The job market for data science and AI professionals is booming across the world, making it a desirable career choice. AI engineers and data scientists are both intertwined job roles and have the potential to help a professional leverage rewarding career growth opportunities.
Whether you’re a fresh college graduate entering the IT industry, or have been recently laid off amid the coronavirus pandemic, or have been temporarily furloughed or are worried about upgrading your skills for career growth, there is no better time than this to pick up some data science and AI-related skills. If you’re considering a career in data science and artificial intelligence, let Springboard be your go-to resource to launch a career in data science and artificial intelligence. Springboard offers comprehensive, 1:1 mentored data science and artificial intelligence online programs to help professionals up-skill and fully harness these career growth opportunities.