Embarking on a Machine Learning Career? Here’s All You Need to Know

Embarking on a Machine Learning Career? Here’s All You Need to Know

Walker Rowe

Published on November 9, 2016


A fairly recent phenomenon, machine learning has emerged from the shadow of data science to become one of the most-exciting career domains today. At its core, it’s all about enabling artificial intelligence (AI) to algorithmically learn from past experiences, in much the same way a human being would.

It’s not difficult to imagine the virtually endless applications for a technology like this!

In this article, we explore the market for skilled machine learning specialists, touching upon the companies that hire machine learning (ML) data scientists, how much employees can expect to earn, and their responsibilities will be.  We will also discover the importance of sound certification training in machine learning and how the traditional role of the data scientist has been turned upside down because of the growth of big data tools and programming frameworks.

Also Read: What is machine learning, and why does it matter?

Data Science v/s Machine Learning

First, it is necessary to define what we mean by data science as that can vary a lot from one company to the next and from one position to another.

Data scientists used to be hired mainly by universities and companies like pharmaceutical companies where they ran clinical trials.  They did pure mathematics and statistics.  Those data scientists worked with the R programming language and statistical and machine learning tools like Matlab.  Most of these data scientists had masters or PhD degrees.

But the invention of Hadoop in 2003 and Spark soon thereafter changed all that. These tools made it much simpler to apply machine learning to business.  In this environment, data scientists are now expected to know programming languages too. Or if they do not, they will earn less.

Modern Tools that Simplify Decades-old Data Science

Apache Spark includes a machine learning library, called Spark ML.  And the Python programming language includes Scikit-Learn ML API.  Scala support ML as well.  These tools not only make it easier to apply data science to business problems, it also makes it easy to adapt algorithms to process large amounts of unstructured data.  This is something that data warehouses and BI systems that came before could not do.

And the sources of data that data scientists are expected to work with have changed as well.  Programmers can tap into topics trending on Twitter to monitor brand loyalty and perception.  And everyone from cell phone companies, online newspapers, to retailers are offering data about their customers – for a price!

All of this has meant that data science is no longer an academic subject, restricted to universities and R&D units in industry.  Now advertising companies, retailers, and manufacturers could apply machine learning to business problems, and the need for data scientists arose.  But skilled data scientists are harder to find.  So regular programmers seek to move into this field.  And PhD statisticians are looking to learn programming.

What Should I Learn to Get Started as a Data Scientist Specializing in Machine Learning?

These big analytics platforms need different types of skills.  Generally these are grouped into data science and big data engineer roles.  The data scientist takes ideas from business and works to wrap classification and predictive models around those. The big data engineer and data scientist work to implement those ideas as code.

Data scientists work with cloud tools like Jupyter or Zeppelin notebooks to interactively massage and select data, create subsets, join sets, and then run ML algorithms against that.  They can do that directly with big data distributed databases.  Or they can do that offline using spreadsheets and then hand the implementation details over to big data engineers.

The big data engineer understands how to deploy Spark, Hadoop, and Kafka across distributed systems using tools like Ansible, Mesos, Yarn, and Docker. And they do all of this with code.

All of this means that it is no longer sufficient for the data scientist to just know R or Matlab.  Now they are increasingly expected to know how to abstract their work using programming languages like Python and Spark and their respective ML libraries. This is very different from running an algorithm over a spreadsheet. 

What business wants is a ML data pipeline that runs in an endless loop to yield insights for how they should set prices, adjust product mix, etc.  It is no longer sufficient to create a graph or print out a model and hand that to the business manager.

Salaries for Machine Learning Specialists

Thus the data scientist has become more valuable to business.  So how much can they earn?

Salaries vary by location.  Data scientists and programmers of all types earn far more in the USA than in India.  And salaries in the San Francisco area are far ahead of the rest of the USA. 

We can say something about average salaries based on published surveys.

Datajobs reports that data scientists – including machine learning experts - can earn from $85,000 to $170,000.  Glassdoor lists average salaries at individual companies.  Airbnb pays $123,724.  Twitter pays $135,402.

Where to Find Work as Machine Learning Expert

Most data scientists and machine learning specialists find work on the usual job portals, including Indeed.com, Glassdoor, Monster, and Dice.  Microsoft list its machine learning jobs here. And to break into the field, you can find individual contracts at Upwork.

The Best Companies to Work For if You’re a Machine Learning Expert

There are two types of companies to consider when looking for machine learning jobs:  the large, established companies, and start-up businesses.  And there are companies that make data science their primary business and others which have data science departments. In addition, there are two markets for machine learning experts: clouds that let customers upload data and logs for analytics and companies that offer APIs and other tools to let customers write their own algorithms.

The vast majority of startup businesses for machine learning experts are based in California.  Some of these, like Hydrosphere, have a presence there but have their developers offshore.  You would need to speak Russian to work at Hydrosphere.  Firms that handle large data sets and have machine learning departments like the Bank of America or Accenture are also a great choice.

The larger companies that dominate the data science cloud business in the USA include Databricks and IBM Watson Analytics.  Google has its Google Prediction API.  If you cannot land a job at Google, you could browse their list of partners and apply to any of those.

So the takeaway message is that the demand for data scientists is soaring because of the spread of big data. With high quality certification training in machine learning, you learn the ropes with industry mentorship and course content from a team of global influencers so you are industry-ready by the time you graduate. 

About the Author

Walker Rowe is an American big data programmer and writer. As a tech enthusiast, he covers a wide range of technology topics for online publications.


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