If you have to write an artificial intelligence (AI) or machine learning (ML) job description, it can be difficult to convey precisely what kind of new employee you want to hire. Doing so requires using the right language, plus understanding what type of role is most appropriate for what you want to achieve.
To guide you through the challenging process of recruiting top AI talent, we’ll start by looking at the differences between different AI & ML roles. Then, we’ll discuss who should be your first hires depending on the approach you choose for your ML projects. We also recommend you make sure that you don’t do these seven things to scare off the AI talent you’re trying to hire.
The Differences Between Popular AI & ML Jobs
Someone who’s unfamiliar with the various job titles associated with AI & ML may quickly get overwhelmed by the perceived lack of distinction between them. This breakdown should help.
Machine Learning Engineers
Machine learning engineers oversee all parts of machine learning projects, from developing the ML models to managing the related infrastructure. The descriptions for these jobs typically expect successful applicants to know programming languages (Python is usually a must), computer science, data modeling, and state-of-the-art approaches in the relevant AI areas (e.g., natural language processing, computer vision).
Machine learning engineers also need to work well with others, particularly since data scientists and engineers often assist them with projects. To get an idea of the overall uptick in machine learning job listings seeking engineers, consider that there was a 344% rise in these positions from 2015 to 2018.
Data scientists spend most of their time cleaning and preparing data before they can start looking for patterns in the data and modeling with it. Although data scientists don’t usually deal with customer-facing applications, some of the conclusions they make could have a significant impact on business success. For example, the outcomes of a data scientist’s work could aid decisions about sales and marketing strategies, inventory management, supply and logistics.
Many people who go on to become data scientists have majors in subjects like statistics. However, there is a growing number of data science majors that a person could pursue. Also, formal education for a data scientist frequently continues beyond a bachelor’s degree. Statistics indicate that 88% of data scientists have at least master’s degrees, and 46% have doctorates.
Data scientists are usually expected to have experience with statistical software (e.g., Python, R), database languages like SQL, statistical forecasting, and ML modeling. Data scientists should be excellent communicators, so that they can clearly understand the expectations of management and explain in detail what can be achieved with data science and machine learning. Analytical and problem-solving skills are also crucial for a data scientist role.
Whereas the jobs covered so far are often part of commercial workforces, research scientists are more interested in pursuing scientific discovery rather than looking for industrial applications. They usually experiment with cutting-edge approaches, and so the impact of their work is mostly observed in the long run.
Considering the nature of scientific work, continual curiosity is a trait that serves AI research scientists well. They must also be unafraid of failure, especially since some of their efforts are in such early stages that it’s hard to see how they might turn out.
The people who choose this path are expected to have a Ph.D. in computer science or a related discipline as well as a publication track record in top AI conferences. Other qualification requirements usually include solid programming language skills for prototyping, experience with deep learning frameworks (e.g., PyTorch, TensorFlow), and familiarity with the state-of-the-art approaches in the relevant AI subject areas.
Applied Research Scientists
Unlike the researchers covered above, these professionals deal with both research and the development of industrial applications. This role is perfect for people who want to stay on the cutting edge of what’s new and still see the results of their work through deployed AI applications.
Applied research scientists should have at least a Master’s degree in computer science or a related technical field. However, a Ph.D. is usually preferred. Additionally, these professionals are expected to have strong programming skills and experience with major deep learning frameworks. Excellent problem-solving and data analysis skills are also important.
Data engineers take responsibility for the infrastructure that collects, processes and stores the data in question. Plus, data engineers sometimes help develop algorithms that make raw data more accessible.
The purpose of this role is to take care of all the infrastructure requirements, providing data scientists, researchers, and machine learning engineers with the information they need to work efficiently and draw the right conclusions.
Data engineers are usually expected to have experience with SQL and relational databases, ETL design and implementation, and knowledge of machine learning concepts and processes. These experts should also have an understanding of backend development and software engineering.
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Understanding Who to Hire Depending on the Project and Approach
Now that you understand more about the job titles themselves, it’s time to discuss who to hire first and how to build a strong ML team. Your choices will depend on the nature and timing of your ML projects. Thus, the people you hire for a particular AI & ML project at the start may not be the same people working on it at its completion. Projects evolve and so do the teams that work on them.
Here’s a look at some factors to consider depending on the goals of your ML projects and the approach your company chooses with regard to machine learning applications.
Getting Insights from Internal Data
If you have an extensive collection of internal data and are ready to start using it extensively for making business decisions, you may want to start by building an in-house data science team. The team can consist of a couple of data engineers and data scientists headed by a chief data officer or head of data reporting directly to your chief technology officer (CTO).
Implementing External AI Solutions
Company decision-makers who have their sights set on working with external AI solutions should start by hiring a couple of machine learning engineers. The people you hire must understand the data you possess and be able to evaluate the third-party AI solutions and their fit to your business needs. These ML engineers should also have the skills to implement the external AI solutions that have been selected.
If you have multiple ML projects involving many third-party AI APIs, you may also want to hire a team leader who will oversee everything related to AI applications.
Developing and Deploying In-House AI Solutions
Today many companies choose to develop and deploy ML models internally. This approach allows the implementation of solutions that are personalized to the company’s needs.
Leaders that want to take this approach should start by hiring a chief AI officer reporting directly to the CTO. After understanding the expectations of top management and recognizing the company’s AI opportunities in terms of the availability and quality of data, this person can suggest the action plan for further recruiting of ML professionals.
Be prepared to hire several data scientists and ML engineers and realize that you may need more for bigger projects. If you have the ambition to develop and deploy cutting-edge AI solutions, applied research scientists might also be recruited.
Filling the AI & ML Jobs at Your Company
Keep this overview in mind as you progress with your hiring process. No matter whether you want to hire for five machine learning jobs immediately or are interested in posting a couple of artificial intelligence jobs over the next six months, this information should help you succeed with recruiting top AI talent.
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