Have you ever been in a situation where you spend a lot of your time and energy learning
something, only to realize that the skills you have gained don’t match or live up to
the requirements listed by the Employer? If this happens to you, you’ll have to begin
learning new technologies and skills to get to the interview, which is one of the most
painful and tedious tasks in the job searching process. Unfortunately, many people go through
this tiresome loop. With the recent buzz around machine learning,
many courses have come into existence offering a broad curriculum. This leaves job seekers
confused about what they really need to learn to become Machine Learning Engineers. So,
today we will try to find a solution and put you one step ahead of your rival ML job seekers.
After our previous analysis of Data scientist job descriptions, we have received numerous
requests from people asking about a similar analysis on Machine learning. That is why
we conducted this analysis in an identical manner - by leveraging job boards data.
We analyzed more than 500 recent Machine Learning engineer job postings, and this analysis was
mainly focused on the USA. Now, let’s set our expectations straight
from the start. We will try to answer the most common questions every Machine learning
Engineer enthusiast needs to know. • What is the most sought-after educational
background to become an ML Engineer? • What are the most important skills needed
for a Machine learning engineer? • What is the experience required by employers?
• Which firms are offering more opportunities in the field?
• Which are the locations that offer most opportunities?
If any of the above questions are on your mind, stay tuned because you’ll find the
answers by the end of this video. Alright, let’s get started.
First off - education. What is the most sought-after educational
background? Well, this is one of the most common questions
among job seekers because there is a lot of confusion in the job market. Nobody has a
clear idea about the ideal educational background required to become a Machine Learning engineer.
So, let’s see what the data tells us. According to our research:
• Most of the job postings require a Master’s degree
• There are almost as many listings asking for a PhD as the ones looking for a Master’s
degree. • Bachelor’s is the last on the list,
but still has a very good number of openings. In addition, what is worth noting is that
most of the job ads are flexible in terms of the type of degree. For example, very often
we can see Bachelors’s as required and Master’s/Ph.D. as preferred.
What about degree specialization then? Well, it appears that Computer Science, with
Statistics and Mathematics as the not-so-close second and third place are the three specializations
employers are looking for the most. Electrical engineering and physics are the other two
most frequently sought for degrees. Now that we’ve covered the degrees and fields
of study required to become a Machine Learning engineer, let’s take a look at the companies
that are actively recruiting. Who are they? Here are the top 10 companies in our dataset
with the most openings. As you can see, Apple undisputedly tops the
list with almost 60 available offers, followed by Twitter, Amazon, Facebook, Snapchat, and
TikTok. These are some of the most exciting firms in tech field, which extensively rely
on machine learning to run their platforms. So, no surprise here.
Regarding company size, it is obvious that the majority of the offers are coming from
big firms with more than 10,000 employees. However, there is a considerable number of
postings by both mid-range firms (1000 to 10,000 employees) and smaller firms (with
less than 500 employees). Next in our study, we analyzed the industries
with the highest concentration of for Machine Learning engineer job offers. What did we
discover? Unsurprisingly, there are more postings in
the IT and Retail/Wholesale industries at the moment. But these are far from your only
options, as there’s a substantial number of offers in the Consulting, Education, and
Finance industries, as well. Alright! This gives us an idea about the companies
hiring ML engineers. Let’s take a look at geography, shall we? Here, we split the data
based on the state and city where the offers came from:
In terms of states, the majority of Machine learning offers (almost 50% of our data) are
from the state of California. After California, there seem to be a good
number of opportunities in New York, Washington and Massachusetts.
If we consider cities where these jobs were available, we can see three important findings:
1. There seem to be more offers in San Francisco and Santa Clara Valey.
2. There are a considerably good number of offers in New York City and Mountain View.
3. 16 postings, didn’t mention a particular city.
Okay! Now that we’ve outlined the landscape for ML engineer job postings, it’s time
to pay attention to one of the crucial factors to land this lucrative job – working experience:
According to the data, there are generally more offers for people with at least 2 years
of relevant experience. For comparison, there seem to be more offers in the range of 1–5
years of experience and fewer opportunities for 5+ years-of-experience candidates and
freshers at the moment. And that’s certainly good news for those of you who considered
many years on the job as a hard prerequisite for this position. But let’s elaborate on
the experience factor a bit more – this time in relation to degrees.
On average, the experience required with a Bachelor’s degree is 4 years, while for
Master’s degree, it’s roughly one year less - 3 years.
On the other hand, if you hold a PhD, then you’ll need 2 years of experience. However,
there is a little catch here, as most of the recruiters haven’t mentioned the required
experience for Ph.D. holders specifically. They mentioned it in a generalized way like:
Required 2+ years of experience with education in MS or Ph.D. So, overall, if you have a
Bachelor’s degree, you stand a pretty good chance with ML employers, provided that you
have worked for a few years and you have acquired some valuable experience.
Alright! It’s time to dissect the most practical aspect of landing an ML engineering job – the
required skillset. In terms of general skills for the Machine
Learning Engineer position, we discovered the following:
To be a Machine learning engineer, obviously, Machine learning is the primary skill required.
In addition, most of the jobs have mentioned Deep learning and its fields like Natural
Language Processing (NLP) and Computer Vision as a requirement. But that’s not all! There
have been plenty of mentions of Data analytics, Statistical modeling and Data visualization,
as well. Big Data, version control tools like Git and deployment tools like Docker have
been requested in quite a few descriptions, too.
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Back to our topic, how about we dive deep into each type of skills required?
Starting with Programming Languages. No surprise here - Python is leading the chart
with a significant number. What’s worth noting is that C++ and Java are mentioned
more frequently than R, and SQL is mentioned in quite a few jobs, as well.
Continuing with the most sought-after skills, we can’t skip Deep Learning Frameworks:
Tensorflow is leading our chart with Pytorch as a close second. Then the top two are followed
by Caffe and Keras. Tensorflow and Pytorch definitely look as the two most popular frameworks
at the moment. What about packages?
Being able to work with different packages that are suitable for the task at hand is
an essential skill for an ML engineer. So, let’s examine the most frequently requested
ML packages for Python. Scikit-learn, where most of the Machine learning
algorithms and all other important functions are available, is listed as the top package,
followed by Pandas – one of the important libraries for all data manipulation activities.
In third place, we have Numpy and Scipy where, basically, all the important math functions
reside. Okay. It’s time to take a look at Big Data
Technologies: Spark tops the list with a significant lead
over Hadoop, while Hive and Kafka have been mentioned in fewer job postings.
In terms of Cloud Technologies, AWS is the most in-demand cloud technology at the moment
with Google’s GCP and Microsoft’s Azure following in its footprints.
Are Data Visualization Skills important for an ML Engineer?
According to the data – not really. In fact, there are very few mentions of Data visualization
tools for Machine learning jobs. Tableau was mentioned just 15 times, whereas Power BI
only 2 times, which makes it clear that the default packages in Python should suffice
for aspiring ML engineers when it comes to data visualization.
And, last on the list of ML Engineer job requirements come communication skills.
This one is slightly different than all the other skills we have seen until now. Apart
from regular technical skills, communication skills appear to be equally important. Let’s
see how many jobs have mentioned strong communication skills explicitly.
220 jobs have a mention of communication skills as a definite requirement for the desired
candidate. Now, you’ve got a good idea about the skills
and education required to land a Machine Learning Engineer job. One last piece of advice from
our side: knowing technology is one thing and applying it is a whole different thing.
So, to be successful in the ML field, learn the most mentioned important skills first.
Then try to solve a real-world problem by combining all your skills to get a more real-life-like
experience. Remember Machine learning is a very dynamic
field, so be ready to upgrade yourself every day.
If you have any doubts or suggestions, feel free to leave them in the comments. We are
happy to help. Cheers!