Table of Contents
1Machine Learning Jobs
The term “machine learning” immediately invokes images of data science in the minds of most listeners. Growth in the discipline of data science has been phenomenal during the past decade. People from all walks of life and stages of education are competing for data science jobs. Most of them learned everything they needed to know from books and videos available online.
The field of data science has emerged as the most sought-after position for machine learning experts. There is a lot of excitement about the industry because of the prospect of a high salary and adaptable schedule.
However, if you’re interested in a career in machine learning, you shouldn’t assume that your only choice is data science. Many new machine learning positions have emerged as a direct result of the exponential growth in the volume of data acquired by businesses in recent years. This post will outline several promising machine learning-related employment paths for 2023.
2Top Machine learning Jobs in 2023
Machine learning is used in many everyday situations, such as traffic warnings on smartphones, facial recognition software, and websites that propose products based on your browsing history and preferences. Machine learning (ML), a subfield of AI, is getting a lot of attention for its potential to revolutionize the field. The fact that it’s a profitable professional path is an extra incentive to learn more about it. Indeed.com ranks employment in machine learning as the highest paying and fastest growing in the United States, with a growth rate of 344%. The leading machine learning careers of 2023 are shown here.
To acquire a better understanding of what a data scientist’s function comprises, let’s first examine it. Data scientists are those that contribute strategic value to a company through data. A data scientist’s job is to analyze mountains of information in order to find the answers that will help a company go forward. You’ll also need to use machine learning modeling approaches to generate growth-driving projections for the company.
3.1As a Data Scientist, the tasks to work on includes:
Having a solid knowledge of at least one programming language, typically R or Python is a must for aspiring data scientists. You should also be able to use SQL to extract and modify data, create machine learning algorithms, and conduct statistical analyses of large datasets. Data science teams at businesses frequently make use of Numpy, Pandas, Matplotlib, and Keras for data analysis and model construction. Some data science interviewers will evaluate your proficiency with this software, so getting familiar with them is a smart idea. Datacamp offers two excellent career paths to get you started in data science: Data Science with Python and Data Science with R.
3.2Skills Needed to become Data Scientist
Also, as a data scientist, you’ll need to be able to convert business needs into effective machine learning models. To accomplish this, you must have an in-depth understanding of your chosen profession. For example, if you’re interested in a career in marketing, familiarizing yourself with common marketing metrics and terminologies will help you better understand the business challenge at hand and guide your analysis. This Datacamp course on marketing analytics will give you the edge you need to set yourself apart from other students interested in pursuing careers in data science.
3.3Average Annual Salary
Glassdoor reports that the average income for a data scientist in the United States is $143,971. This number rises to between $150,000 and $170,000 annually in major technology firms like Google, Meta, and Apple.
4Data Science Consultant
Working for a consulting firm, your role as a data scientist consultant will be to help develop machine learning and AI-based solutions for their customers.
Typically, a data scientist will work for a single company in a certain industry, where they will focus on resolving internal business issues. As a consultant, though, your job will span various sectors and industries.
Experts in the field of data science can be split into two categories. The first is a consultant for a machine learning approach who proposes an AI-driven solution to the client’s problem but does not carry it out. Examples of such companies include the aforementioned McKinsey and BCG, whose consultants focus on conceptualizing solutions rather than constructing full-fledged systems. A constructor is the second category of data science consultant. This group includes businesses like Deloitte and Accenture, whose data scientists develop fully working AI products for their customers.
4.1Skills Required to become a Data Science Consultant
A conventional data scientist’s responsibilities and those of a data science consultant are highly similar, hence both positions require a wide range of relevant skills. Both roles require competence in machine learning algorithm development, data analysis, and contributing to the company’s bottom line.
However, because they must communicate with clients, data science consultants are also expected to have excellent presentation and communication skills. A consultant’s greatest asset is their capacity to distill complex information into ideas that are accessible to those with less technical training.
In addition, consultants need to be proficient in a wide range of tools due to the diversity of the projects and customers they serve. McKinsey, on the other hand, typically needs candidates to be skilled in both languages to become a consultant, whereas most businesses specify either R or Python as a necessity to gain a machine learning job.
4.2Average Annual Salary
The average annual compensation for a data science consultant is $112,595 (as reported by the job site Glassdoor). Data science consultants at leading firms like McKinsey and BCG, meanwhile, earn yearly salaries of $150,000 to $200,000.
5Machine Learning Engineer
Data scientists focus on creating these predictive models, while machine learning engineers create scalable AI applications that end users can engage with.
When compared to a data scientist, a machine learning engineer takes a somewhat different approach. Data scientists are responsible for analyzing data, developing prediction models, and writing code in R or Python to help a corporation with an issue. Their job involves extensive statistical analysis with the goal of providing valuable commercial insight.
Machine learning engineers, on the other hand, are the ones tasked with constructing and fine-tuning these systems. They may also engage in MLOps activities, such as putting these models into production and, if necessary, constantly monitoring and retraining prediction algorithms.
If you want to become a machine learning engineer, this is an example of the kind of work you may expect to do:
You land a job on the product team for a music streaming service. Putting this model into production requires you to construct a pipeline for a recommendation system. The application you roll out should take in user input and then generate specific suggestions for each consumer based on their current taste in music. Constant model monitoring and retraining of the recommender system are also required.
5.1Competencies needed for a machine learning engineer
Machine learning engineers are required to have expertise in both data science and software engineering, given their position at the confluence of the two fields. You should study statistics, probability, and the basics of machine learning modeling if you want to work as a machine learning engineer.
Since you’ll be building scalable, user-facing applications, you should also be familiar with software engineering concepts like abstraction, modularity, and version control.
Last but not least, a machine learning engineer needs to be familiar with MLOps and the best practices for deploying data science models.
5.2Average Annual Income
Machine learning engineers can expect to make an average of $131,001 per year in the United States, as reported by pay information site Glassdoor. However, major companies such as Meta, Netflix, and Apple pay its ML engineers a starting salary of above $150,000, making them competitive with their data scientists.
Predictive models developed by data scientists are put into production and scaled up by MLOps developers. One of their main responsibilities is taking data scientist code and making it usable in a final product.
6.1As an MLOps engineer, you may expect to work on tasks such as the following:
You get hired by an airline, and there, the data scientists develop a machine learning system to determine which passengers are most likely to buy flight insurance. All of the modeling work is done in a Jupyter Notebook, and then the notebook must be embedded into the business’s website.
Your solution, once implemented, should allow the customer to be guided to various website touchpoints in response to their activities. If a machine learning system determines that a customer is more likely to buy insurance, then that customer will be sent to a page showcasing various flight insurance options.
After the machine learning algorithm has been deployed, a method must be put in place to periodically check on how well it is performing. Since real-world data is in a constant state of flux, the prediction model’s efficacy may deteriorate over time. Regular monitoring of metrics and logs is required to identify problem areas, and retraining may be necessary if the model fails to perform satisfactorily in production.
You should also update your data and models as needed. The training dataset and the predictive algorithm should be versioned so that at any time a previous version can be restored.
Last but not least, it is your responsibility as an MLOps engineer to ensure that the security of your system’s users’ personal information is not breached. Introducing efficient model reporting capabilities, as well as access control measures, and ensuring that newly built infrastructure complies with relevant policies, can all help you reach this goal.
6.2Skills needed for an MLOps engineer:
Unlike data scientists, you probably won’t have to start from scratch when it comes to developing predictive algorithms. However, you should still equip yourself with machine learning packages such as Tensorflow, Keras, and PyTorch.
As much of your job will require refining the codes of data scientists and getting them suitable for production, a foundational understanding of ML techniques is also necessary.
Since automating machine learning operations is your primary responsibility, you should be familiar with software development and MLOps concepts like continuous integration and continuous delivery pipelines.
6.3Average Annual Income
The average annual income for an MLOps engineer in the United States is $118,278. However, in more substantial enterprises like The Walt Disney Company, this figure might rise to over $150,000 yearly.
7Machine Learning Instructor
As soon as you’ve mastered the material, you may begin producing content to help those who aspire to work in machine learning. There is a diverse group of learners who have taken to the internet in an effort to educate themselves in the vast realm of machine learning.
If you have ideas for content you’d like to contribute to the machine learning community, YouTube and Udemy are excellent places to begin. All of them are fantastic options for supplementing a regular salary with your knowledge and experience.
7.1Skills Needed for a Machine Learning Instructor
Teaching machine learning effectively requires excellent communication skills and the ability to explain complex ideas to students who are not experts in the field. Experience in the field of machine learning is usually preferred but is not always required if you can show that you have a thorough understanding of the material.
7.2Average Annual Salary
A machine learning trainer in the United States can expect to make an average of $128,812 per year, as reported by Glassdoor. Because most organizations rely on contract workers or pay teachers a percentage of their class fees, this number is subject to change. Top Udemy machine learning teachers like Jose Portilla make between $1.1 and $4.4 million from their courses alone.
8Other Important Machine Learning Jobs
Robots’ goals can range from efficient task completion to convincing human mimicry. As a result, it’s not surprising that engineers working in robotics find that knowledge of machine learning is extremely helpful.
Software engineers make everything from smartphones to desktop applications. They analyze user feedback data with machine learning to foresee how people will interact with a product’s features.
8.3Specialist in Natural Language Processing (NLP)
A natural language processing scientist develops algorithms to deduce the linguistic conventions that allow humans to communicate with and be understood by machines.
9To wrap up
It is clear from the aforementioned career paths that machine learning is an exciting and diverse field that offers opportunities for people from many walks of life. Jobs in the industry often pay well, with a median annual salary of $100,000.
If you’re looking for a machine learning position, data science is one possible career path, but it’s not the only one. Take your time finding a career that interests you since it is crucial that you choose a work that is in line with your values and aspirations.
You might do effectively as a data science consultant or instructor, for instance, if you appreciate regular interpersonal interaction and have a persuasive personality. If you are more of a technical person, though, and you take pleasure in programming and developing whole products, you may want to think about a job in machine learning or MLOps engineering.
If you have a passion for a certain area of AI, like speech recognition or computer vision, you may find fulfillment in a specialized function, such as machine learning research. Has the prospect of contributing to the expansion of Tesla’s object detection system to enhance the car’s self-driving capabilities ever piqued your interest? If yes, you might want to choose a career as a computer vision engineer. There is a possibility for advancement and a good salary in any machine learning position. No single line of work can be said to be objectively superior to any other; instead, one must rely on one’s own preferences and skills while deciding on a future profession.