The Top Machine Learning Jobs in 2022 and How to Get Them
When most people hear the words “machine learning”, the first thought that comes to their mind is data science.
Dubbed the “Sexiest Job of the 20th Century” by Harvard Business Review in 2011, the field of data science has witnessed massive growth in the past decade. Students, graduates, and working professionals from different backgrounds rushed to break into the industry and land a data science job. Many of them did so successfully just by taking online courses and teaching themselves the subject material.
Data science is currently the most popular machine learning role. The field has a lot of hype around it due to the promise of a thick paycheck and flexible working hours.
However, if you are looking to land a job in machine learning, you should know that data science is not your only career option. The amount of data collected by organizations has grown exponentially in the past few years, and this has, in turn, led to the rise of many new machine learning roles.
In this article, we will break down a few machine learning career options worth pursuing in 2022.
The 7 Best Machine Learning Jobs
Below, we've highlighted some of the top jobs in machine learning in 2022. We've also picked out some of the essential skills you'll need to work in these roles, as well as courses that can help you get the relevant jobs. If you're looking for the best machine learning opportunities in the data industry, check out DataCamp's Jobs to find roles tailored to your skills.
1. Data Scientist
First, let’s start by exploring the role of a data scientist in order to get a grasp on what the job truly entails.
Data scientists are individuals who add business value to an organization with the help of data.
As a data scientist, you should be able to collect, pre-process, and analyze large amounts of data to come up with insights that solve a business problem. You will also be required to employ machine learning modeling techniques to come up with predictions that drive business growth.
Skills required to become a data scientist:
Data scientists are expected to have a strong grasp of at least one programming language - generally R or Python. You should also be able to extract and manipulate data with SQL, build machine learning algorithms, and analyze datasets using statistical techniques. Python packages such as Numpy, Pandas, Matplotlib, and Keras are commonly used by data science teams in companies for data analysis and model building. It is a good idea to learn to work with these packages as some data science interviewers will test your knowledge of them. If you would like to develop your skills in data science and land a job in the field, Datacamp has two great career tracks to help you get started: Data Science with Python and Data Science with R.
As a data scientist, you will also be required to translate business requirements into functional machine learning models. In order to do this, you need to have a grasp of the field in which you work. If you would like to work in marketing, for example, it is a good idea to learn some frequently used marketing metrics and terms, as this will help you better understand the business problem at hand before coming up with an analysis. You can take this marketing analytics course by Datacamp to gain domain-specific knowledge and stand out from other data science aspirants.
To get a better understanding of what companies expect from data scientists, take a look at this job listing by HP:
According to Glassdoor, the average data scientist’s salary in the US is $143,971 per year. At large tech companies like Google, Meta, and Apple, this number increases, ranging from $150,000 to $170,000 annually.
2. MLOps Engineer
MLOps engineers productionize and scale predictive models built by data scientists. Their job is to convert data science code into a functional end product with which users can interact.
Here is an example of the kind of task you will work on as an MLOps engineer:
You join an airline company, and the data scientists there build a machine learning algorithm to predict users who are most likely to purchase flight insurance. The entire model is created in a Jupyter Notebook, and you are required to embed it into the company’s website.
The system you build should be able to redirect the customer to different touchpoints on the website based on actions they take. For instance, if the machine learning algorithm predicts that the client is likely to purchase insurance, they will be redirected to a web page featuring different flight insurance plans.
After deploying the machine learning algorithm, you need to implement a process that can continuously monitor model performance from time to time. Real-world data is always shifting, due to which the predictive model can degrade. Metrics and logs must be checked occasionally to understand where things are going wrong, and if the model doesn’t perform well in production, it might have to be retrained.
You also need to perform data and model versioning when required. Any amendments to the training dataset or predictive algorithm must be tracked, and previous versions should be kept to ensure that they can be restored at any time.
Finally, as an MLOps engineer, you must verify that the system you build is secure, and that no sensitive user data is compromised. To achieve this, you can implement access control mechanisms, verify that the infrastructure created meets compliance policies, and introduce effective model reporting capabilities.
Skills required to become an MLOps engineer:
As an MLOps engineer, you will generally not be required to build a predictive algorithm from scratch. However, you will still be expected to work with machine learning libraries like Tensorflow, Keras, and PyTorch, so make sure to add these to your toolbelt.
You also need to understand the fundamentals of ML algorithms since much of your work involves refactoring data scientists’ codes and making them production ready.
Finally, because your main task is to automate machine learning workflows, you need to have an understanding of software development and MLOps concepts such as CI/CD pipelines.
For a better understanding of exactly what companies expect of an MLOps engineer, take a look at this MLOps engineer job listing by Manifold:
The average MLOps engineer salary in the US is $118,278 per year. In larger organizations like Walt Disney Company, however, this number can go up to $150K annually.
3. Machine Learning Engineer
While a data scientist’s job is centered around building predictive models, a machine learning engineer designs scalable AI products that end users can interact with.
There are a few differences between a machine learning engineer and a data scientist.
Data scientists write code primarily in R or Python, analyze data, and build predictive models to solve a company’s business problem. Much of their work is highly statistical and revolves around generating business insight.
On the other hand, machine learning engineers are responsible for building and training machine learning pipelines. In some cases, they also perform MLOps tasks such as taking these models into production and continuously monitoring and retraining predictive algorithms when necessary.
Here is an example of the kind of task you will work on as a machine learning engineer:
You get hired at a music streaming company and join the product team. You are required to build a recommendation system pipeline and ship this model into production. The application you deploy should ingest user data and provide each customer with personalized recommendations based on their existing music preferences. You also need to constantly monitor model performance and retrain the recommender system when necessary.
Skills required to become a machine learning engineer:
Machine learning engineers sit at the intersection of data science and software engineering, and are expected to possess an understanding of both domains. If you’d like to become a machine learning engineer, make sure to learn statistics, probability, and the fundamentals of machine learning modeling.
You also need to understand software engineering principles like abstraction, modularity, and version control, as you will be creating scalable applications with which end-users interact.
Finally, as a machine learning engineer, you need to possess knowledge of MLOps and must be well-versed with the best practices involved when taking data science models into production.
Here is a screenshot of Spotify’s machine learning engineer job listing:
According to Glassdoor, the average machine learning engineer salary in the US is $131,001 per year. However, large corporations like Meta, Netflix, and Apple offer a base salary of over $150,000 for a ML engineer role, which is on par with their data scientist salaries.
If you’d like to learn the skills necessary to become a machine learning engineer, Datacamp’s Machine Learning Fundamentals with Python course is a great place to start.
You can also read this article to gain further insight into the role of a machine learning engineer and how you can become one.
Disclaimer: There is a lot of overlap between the role of a data scientist, machine learning engineer, and MLOps engineer. While this article provides an explanation of all three roles based on their traditional definitions, it is possible for companies to hire for these job titles interchangeably.
4. Data Science Consultant
As a data science consultant, you will be working with a consulting firm to come up with machine learning and AI solutions for their clients.
In a typical data science role, you work for a single company in a fixed domain and solve business problems within the organization. However, as a consultant, you will be working on many different projects with clients in different industries.
There are two types of data science consultants. The first is a machine learning strategy consultant who comes up with an AI-driven strategy to solve their client’s problem but does not actually implement it. Top-tier consulting firms like McKinsey and BCG fall into this category, and their consultants conceptualize solutions rather than actually building out end-to-end systems.
The second type of data science consultant is the builder. Companies like Deloitte and Accenture fall into this category, and their data scientists actually implement fully functional AI products for their clients.
Skills required to become a data science consultant:
There is a lot of overlap between the role of a standard data scientist and that of a data science consultant. They are both expected to know how to build machine learning algorithms, analyze large amounts of data, and add business value with their expertise.
However, data science consultants are also often expected to have phenomenal communication and presentation skills as they need to interact with clients. The biggest strength of a consultant is their ability to translate data into actionable insights that can be easily digested by non-technical people.
If you are interested in becoming a data science consultant and would like to hone your skills in data storytelling, you can take this Data Communication Concepts course by Datacamp.
Also, since consultants work on many different projects for a variety of clients, they are required to be able to use a wide set of tools. For example, while most companies list either R or Python as a prerequisite to get a machine learning job, McKinsey usually requires candidates to be proficient at both languages to become a consultant.
Here is a screenshot of McKinsey’s job description for a data science consultant:
According to Glassdoor, the average salary of a data science consultant is $112,595 per year. However, top consulting firms such as McKinsey and BCG pay their data science consultants an average of $150,000 to $200,000 annually.
5. Machine Learning Research Scientist
While a data scientist builds machine learning models to drive business value, a research scientist creates custom AI solutions from scratch. A machine learning researcher works in an academic setting or a large firm like Google that funds research.
Machine learning research is theory-heavy, and researchers usually focus on developing new machine learning models or improving the performance of existing algorithms.
There is a stark contrast between machine learning practitioners and research scientists.
In ML research, even a marginal increase of 0.2% in model accuracy can be considered a breakthrough and is worth writing a paper about. In contrast, someone working to implement a machine learning solution for an organization would not really be concerned with a small increase in performance, especially if it is at the expense of company time and computational power.
Also, while machine learning industry practitioners are often generalists who are able to manage end-to-end project workflows, researchers are highly specialized in a single aspect of the field and continue to make discoveries in that specific area.
Skills required to become a machine learning research scientist:
As machine learning researchers are highly specialized academics, they are often required to have a master’s or PhD degree, should be skilled in writing research papers, and must know at least one programming language. Machine learning skills in a specific field such as optimization or regression analysis are also necessary.
Here is a screenshot of Google’s job description for a machine learning research scientist:
According to Glassdoor, the average annual salary of a machine learning research scientist is $151,124.
If you would like to become a machine learning research scientist but are not sure where to start, check out this Machine Learning Scientist with Python learning track by Datacamp.
6. Computer Vision Engineer
As a computer vision engineer, you will develop object detection, face recognition, and pose estimation models for a variety of organizational use-cases.
If you were to work at a company that develops security solutions, for example, you might be tasked with developing an intrusion detection system to identify and prevent threats from taking place in a timely manner.
The role of a computer vision engineer is one that is highly specialized as their area of focus is limited to a single domain. Companies that hire computer vision engineers usually either expect them to come up with a novel solution in the field or improve upon available existing solutions.
This is different from a generalist role like ML engineering where pre-trained models or existing packages can be applied to solve a business problem.
Skills required to become a computer vision engineer:
To become a computer vision engineer, you need to have strong programming skills, and some knowledge of software engineering principles. Learn to solve DSA (Data Structures and Algorithms) questions, as these are frequently asked in interviews.
Since you will be building out computer vision applications, you need to know how to translate the company’s requirements into an end-product. You are therefore expected to have some knowledge of system design.
Finally, learn calculus, statistics, mathematical optimization, and linear algebra, as these are the building blocks of machine learning algorithms. If you are a beginner to machine learning and lack the foundational math knowledge required, you can start by taking Datacamp’s Introduction to Linear Algebra course.
If you would like a better understanding of the skills required to become a computer vision engineer, take a look at this job listing by Tesla:
The average salary for a computer vision engineer role is $121,369 per year according to Glassdoor. In companies like Meta and Apple, this number can rise to $150,000-$200,000 annually.
7. Machine Learning Instructor
After gaining proficiency in the field, you can start creating content on the topic to educate machine learning aspirants. The field of machine learning is broad, and many students from different backgrounds are attempting to teach themselves the subject material online.
Online learning sites like DataCamp are often on the lookout for trainers who are able to cater to these students’ needs, and you can always apply to become an instructor on the platform.
YouTube and Udemy are also great places to start out if you have specific topics in mind that you would like to share with the machine learning community.
These are all great ways to build a stream of passive income with your expertise on top of having a full-time job.
Skills required to become a machine learning instructor:
As a machine learning instructor, you need to have exceptional communication skills, and should be able to break down highly technical concepts to a non-technical audience. You are generally expected to have some experience working in the field of machine learning, but this is not always a hard requirement, as long as you can demonstrate sufficient depth of knowledge in the subject you are teaching.
Here is a screenshot of a machine learning instructor job listing by an online learning platform called FourthBrain:
According to Glassdoor, the average annual salary for a machine learning trainer in the US is $124,812. However, since most companies hire instructors on a freelance basis or pay them based on course revenue, this amount can vary. Top machine learning instructors on Udemy like Jose Portilla, for example, earn approximately $1 million to $4 million solely from their online courses.
Deciding on a Machine Learning Career Path
As you can gather from the career options listed above, machine learning is a field with vast opportunities for people who come from different backgrounds. Jobs in the industry compensate very well, with an annual salary of $100K being on the lower end of the pay scale.
Data science is one of your career options if you are looking for a machine learning role, but it is not the only one. It is important to choose a role that aligns with your goals and values, so take your time in selecting a job that you find most interesting.
For example, if you are someone who enjoys communicating with people on a daily basis and are highly persuasive by nature, you would likely fare well as a data science consultant or instructor. However, if you are a more technical person who enjoys programming and creating end-to-end products, then you should consider pursuing a career in machine learning or MLOps engineering.
Specialized roles such as machine learning research can make fulfilling careers if you are inclined towards a specific area like speech recognition or computer vision. Have you ever felt like it might be exciting to help build out Tesla’s object detection system to improve the car’s self-driving capabilities? If so, a computer vision engineering job might be the one for you.
At the end of the day, all machine learning roles pay well and have room for continuous growth and improvement. There is no job that is strictly better than the other, and making a final career decision solely depends on your own interests and capabilities.
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