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The artificial intelligence (AI) sector has created many jobs for aspiring professionals all over the world. A lot of companies across industries are adopting AI solutions. Enterprises have also recognized the benefits of having an in-house team for data analytics. This has led to the rise of AI-related jobs. However, the different titles present in the market may confuse a newcomer.
Different titles also require different specializations, which makes it difficult for an aspirant to choose the role they are equipped for and interested in. In this article, we will look at the key skills for AI roles, and the specializations required to become an AI developer, researcher, or architect. We will also take a look at the requirements of becoming a machine learning engineer and data scientist in detail.
Table of Contents
Key Skills Required for AI Jobs
- Mathematical and Statistical Skills
- Expertise in Programming Languages and Computer Logic
- Experience in Data Management Software
- Soft Skills
- How To Become an AI Developer
- How To Become An AI Architect
- How to Become a Machine Learning Engineer
- How to Become a Data Analyst
- How to Become a Data Scientist
AI and its related technologies are quickly becoming some of the most sought-after and lucrative career options. Due to the quick growth of the AI market and the mainstream adoption of various AI-based applications, companies are hiring for AI-specialized roles in great numbers.
As with any field, specializations in AI increase the market value of an aspirant. The field has various subcategories, and each one requires a different set of hard and soft skills. Moreover, the varied nature of the field also allows aspirants to choose from job roles that they are interested in.
Key Skills Required for AI Jobs
AI jobs require aspirants to possess a few key skills, regardless of their specialization. As a rule of thumb, companies require aspirants to have at least a bachelor’s degree in computer science or engineering. In addition to these qualifications, they are also expected to have certain key skills for handling AI responsibilities.
These skills stem from the few responsibilities that are common across all job roles in the AI market. Let’s take a look at these critical skills in detail.
Mathematical and Statistical Skills
Various mathematical concepts, especially statistics, are essential for practicing data science, machine learning, and AI. AI is language-agnostic, and the problem-solving methodologies associated with this field stem from mathematical skills. The areas of mathematics applied in AI include calculus, algebra, statistics, and probability.
Owing to the large amount of data processed in AI roles, a deep understanding of mathematical and data wrangling processes is integral for the job. Besides, concepts such as Naive Bayes and Gaussian Mixture models are rooted in the mathematical concepts of probability.
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Expertise in Programming Languages and Computer Logic
Programming languages, such as C++, Java, and mainly Python, constitute one of the most basic skills required for AI jobs. Familiarity with Python is essential for developing AI applications. This is due to the large number of libraries that the language provides.
Expertise in the R programming language is another requirement for data science roles. It allows a candidate to learn different methods to manipulate and clean data while using statistics to derive insights. Moreover, specialized roles require knowledge of specialized libraries in these languages, allowing the aspirant to build the base of the language.
Experience in Data Management Software
In a data scientist role, an aspirant is expected to possess a working knowledge of software used to manipulate large amounts of data. This includes software, such as Hadoop, Spark, Hive, and Pig, which are used for analyzing large databases in the cloud. Moreover, environments where data science and AI are used, require a lot of data. Therefore, it is important for the candidate to be able to manage large databases.
Database management software, such as Structured Query Language (SQL), is also a required skill for data scientists and AI jobs alike. SQL allows for dependable management of databases and easy access to data. Due to this, these languages become integral for those looking to get hired as an AI expert.
Soft Skills
Soft skills are one of the most overlooked aspects when applying for data science or AI roles. Soft skills cannot be learned through training or upskilling; instead, aspirants need to have them intrinsically. These include curiosity to know the inner workings of systems, business acumen for deriving actionable insights, and communication skills for delivering insights to business leaders.
These skills are often utilized on a day-to-day basis on the job, as they can be applied to various parts of the AI field. Moreover, they also increase an aspirant’s chances of getting hired.
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AI Job Roles and Titles
Job roles in the AI market are varied, both in terms of the required skill sets as well as the responsibilities. The AI market creates specializations in the required roles, as it results in a higher degree of compartmentalization and specialized roles.
In a company using AI for data analysis-related tasks, roles are split between the various methods of handling data. These include data analysts, data scientists, and data engineers. These roles involve using AI as a tool, with most of the focus being handling and wrangling data to derive knowledge known as ‘insights’.
However, in the AI field as a whole, individuals specialize in creating algorithms, researching the capabilities of AI technologies, and organizing solutions for use on a larger scale. These are AI developers, researchers, and architects, respectively. This approach of creating specific designations for each part of an AI algorithm’s workflow allows companies to scale quickly by hiring specific talent.
One of the most prominent roles in the market is that of the machine learning engineer. The responsibilities of machine learning engineers include the responsibilities of data scientists and software engineers, with a special focus on using and managing machine learning algorithms.
AI Job Roles
Here’s how you can plan your career path for some of the most prominent roles in the AI domain.
How to Become an AI Developer?
AI developers are hired to provide solutions to various issues using AI and programming. AI developers are responsible for designing, developing, and deploying AI-based systems. This includes keeping up-to-date with the latest advancements in AI and coming up with new ways to improve existing infrastructures.
AI developers are expected to have an intrinsic understanding of AI concepts, statistics, and mathematics, along with expertise in programming languages, such as Python. They are also required to build the accompanying infrastructure of AI models, such as ingesting data, transforming data, and creating data lakes.
This requires AI developers to develop their knowledge of a specific skill set under AI-based skills, especially Python and R programming languages. These languages make up the core of many AI systems. Under Python, knowledge of libraries, such as TensorFlow, SciKit Learn, and Keras are heavily sought-after.
Aspirants are also required to have a working knowledge of the fundamental concepts and principles behind machine learning. Expertise in fields, such as text mining, data pre-processing, and data cleaning is another must-have.
How to Become an AI Architect?
The role of AI architect requires professionals to have a big-picture outlook of AI operations of an organization. The AI architect is in charge of the AI platform, thus providing services for customers while evolving the product according to the changing requirements of the company.
AI architects are responsible for putting AI systems in operation and managing them. This includes creating the required infrastructure and managing the compute needs of the systems. They are also responsible for looking at the data-to-insight-to-action loop closely, thus requiring a bigger outlook of the general state of the company’s operations.
AI architects are also responsible for ensuring secondary objectives, such as the security of the solution, stability of the product in general, and how easy it is to use. Owing to the higher skill level required for this job, aspirants are expected to have experience in other AI-related roles before considering the role of an AI architect. It is recommended to have over five years of experience in a job of the same domain.
This job requires high-level, in-depth knowledge of machine learning concepts and advancements. This includes in-depth knowledge of predictive modeling and analysis, unsupervised learning, graph theory, heuristic optimization, and classification problems. Soft skills pertaining to communication, data visualization, and problem-solving are also required.
Learn More: How to Build a Career in AI and Machine Learning
How to Become a Machine Learning Engineer?
Machine learning engineers are expected to be subject matter experts in their sub-field as well as in devising AI solutions and applying the right solution to a problem. This is more of a generalist approach that requires a professional to have a working knowledge of various fields of AI.
Since this is a mid-level role, individuals can transition from similar roles to this one. Machine learning engineers can also consider a more generalized role in an environment where various concepts of AI can be used in conjunction with each other. The actual problems that organizations face are rarely solved with the help of one specialization.
Knowledge in fields, such as natural language processing, computer vision, deep learning, and visualization, is required for the role of a machine learning engineer. As with any AI role, expertise in data wrangling software, such as Hadoop and Spark and statistical languages like R are also common requirements.
Knowledge of machine learning concepts also comes with the ability to work with Python and C++ efficiently. Depending on the requirements of the job, specialized knowledge in programming libraries relating to specific sub-fields may also be required.
How to Become a Data Analyst?
Data analysts are discrete from machine learning and AI-related roles, as they directly focus on deriving insights from data using machine learning as a tool. Machine learning-related roles focus on the AI solution instead. In a nutshell, data analysts have the capability to transition an ordinary business into a data-driven one.
Data analysts focus on providing data to the individuals that require it and are tasked with analyzing the data and determining its importance. By doing so, they aim to gauge its impact on the larger scheme of a company’s functioning. Overall, they take data and use it to supercharge operations for more efficient decision-making.
Their primary role is to perform data analysis and present the results in a way that is easy for stakeholders to perceive. Using this analysis, data analysts provide information regarding any changes in the company’s market, along with a potential solution to maximize adaptiveness. This makes communication skills one of the must-have skills for a data analyst.
Along with in-depth knowledge of statistics and data management methods, data analysts must make use of effective communication skills to convey information accurately. It has to be done in a way that is easy for non-technical professionals to understand. This makes data analysts the bridge between AI-powered solutions and business decision-makers.
Learn More: What is Artificial Intelligence: History, Types, Applications, Benefits, Challenges, and Future of AI
How to Become a Data Scientist?
Termed “The Sexiest Job of the 21st Century†by the Harvard Business Review, data scientist roles are one of the most sought-after in the AI/ML domain. Data scientists use machine learning as one of the tools in their vast arsenal, with the end goal of deriving useful insights from data and providing them to the company.
Along with cleaning and organizing big data, data scientists make heavy use of statistics to identify patterns in collected datasets. Descriptive statistics and analysis are also a big part of data science roles, as they have to use these to derive actionable insights for companies.
Data scientists use ML and deep learning (DL) models to solve a business need for the company. This requires them to have a specialization in programming languages, such as Python and Java. Machine learning is used by data scientists as a tool for smart automation and as a means to derive helpful insights from large datasets.
Communication is an important part of a data scientist’s role, as they are required to communicate insights to the stakeholders of the company. Data scientists are also required to be familiar with software, such as Tableau, R, and MatLab for data wrangling and visualization needs.
When asked about the most lucrative job role in artificial intelligence, Ryan FoltzOpens a new window , PhD., data scientist, ExabeamOpens a new window , says, “Academia is attractive for a lot of reasons, but it’s a field where it can be extremely challenging to forge a career. That helps to explain why over the last 10 years approximately 60% of new physics PhDs end up taking jobs in areas of industry, such as engineering, computer software and hardware, business, and non-STEM.
As a former astrophysicist and current data scientist in cybersecurity, I’ve seen the overlap within the fields, and it is substantial at a fundamental level.
Emerging technical fields like data science offer an exciting, innovative way to apply the software, math and statistics skills learned from non-traditional sources like astrophysics to make real contributions to the field, which is moving rapidly.
Data scientists have the opportunity to apply cutting-edge techniques to solve real-world, difficult problems. And those studying non-traditionally tech-related subjects possess the core skills needed to solve those problems creatively, proficiently, and efficiently.â€
Talking about the talent shortage in the field, Sergey KolychevOpens a new window , distinguished engineer, WhiteHat SecurityOpens a new window , says, “Research scientist is the most lucrative role and the one that is facing most shortage at present and for a good reason.
It’s quite easy and not time-consuming to become a machine learning engineer because current ML applications and libraries are well documented, easy to use, there’s a lot of examples on how to create an ML model, how to deploy it to production, how to prepare the data, and so on.
So, the ML engineer role has a pretty low barrier for entry. Consequently, there is a lot of proposition in the market, reducing earning potential and shortage. Data scientist is a second in the food chain and has more of earning potential but requires longer time spent on education and work experience, excellent grasp on statistics, and data preparation.
However, one can still be pretty successful in this role by only utilizing off the shelf solutions, libraries, and techniques. But due to a considerable time investment required, potential shortage for this type is more acute than for an ML engineer.
Most lucrative (if this person works in industry and not in academia) role is a research scientist, a role that certainly requires a PhD and multiple published papers in respected journals. Such individuals are able to lead ML teams, develop novel ML techniques from scratch, according to unique client needs.
To become a research scientist, one will require a considerable raw talent and a significant time investment. There are not a lot of these people and as far as I know they are compensated extremely well in the right places.â€
Closing Thoughts for Techies
The ML/AI field has various specializations for aspirants, which means that the pay scale and job responsibilities for each of these roles must factor into their decision. The examples listed here are just a few compared to all the different roles that companies offer today. Specializations definitely make for better market value and experience.
That being said, these specializations must not be a hasty decision. Owing to the varied technology stack and requirements for these jobs, aspirants must choose a job role that they feel comfortable with. There are various online courses for AI specializations so that aspirants can choose something that they are interested in.
In addition to pursuing the right certificate courses, job seekers must also aim to execute a project relevant to their area of expertise. This is listed as a requirement for many job listings and allows employers to determine the quality of an aspirant’s work. Those willing to get into the AI domain must also decide whether they have the required soft skills for the job.
Are you considering a career as an AI/ML professional? What did you think of this article? Comment below or let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!
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