Machine learning is a relatively recent phenomenon that has emerged from the shadows of data science to become one of the most exciting career areas today. At its essence, it enables artificial intelligence (AI) to algorithmically learn from past experience, just like humans can.
It is not difficult to imagine that the applications of such technology are virtually limitless.
In this article, we explore the market for skilled machine learning specialists, touching on companies that hire machine learning (ML) data scientists, how much employees can expect to earn, and their responsibilities. We also discuss the importance of proper certified training in machine learning, and how the growth of big data tools and programming frameworks is upending the traditional role of a data scientist.
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Data Science and Machine Learning
First, we need to define what we mean by data science, as this can vary widely across companies and job roles.
Data scientists used to be employed primarily by universities and companies such as pharmaceutical companies to conduct clinical trials. They were trained in pure mathematics and statistics and used statistical and machine learning tools such as the R programming language and Matlab. Most of these data scientists held master's or doctoral degrees.
But the invention of Hadoop in 2003, and the arrival of Spark soon after, changed everything. These tools made it much easier to apply machine learning to business. In this environment, data scientists were also expected to know programming languages, or they would make less money.
Apache Spark includes a machine learning library called Spark ML, and the Python programming language includes the Scikit-Learn ML API. Scala also supports ML. These tools make it easier to apply data science to business problems and easily adapt algorithms to process large amounts of unstructured data, something previous data warehouses and BI systems couldn't do.
The sources of data that data scientists are expected to work with are also changing: Programmers can use trending topics on Twitter to monitor brand loyalty and brand perception, and companies like mobile phone companies, online newspapers, and retailers are all offering up data about their customers for a fee.
All this means that data science is no longer an academic field limited to universities and industrial R&D departments. Now, advertising agencies, retailers, and manufacturers can apply machine learning to business problems, creating a growing need for data scientists. But skilled data scientists are hard to find, so general programmers are trying to get into the field, and statisticians with PhDs are trying to learn programming.
What should you learn to get started as a data scientist specializing in machine learning?
These large-scale analytics platforms require different types of skills. Generally, these fall into the roles of data science and big data engineers. Data scientists take ideas from the business and create classification and predictive models based on them. Big data engineers and data scientists implement those ideas as code.
Data scientists use cloud tools like Jupyter and Zeppelin notebooks to interactively manipulate and select data, create subsets, combine sets, and run ML algorithms on it. This can be done directly using big data distributed databases, or offline using spreadsheets and handing over the implementation details to big data engineers.
Big data engineers know how to deploy Spark, Hadoop, and Kafka across distributed systems using tools like Ansible, Mesos, Yarn, and Docker — and do all of this with code.
This means that it is no longer enough for data scientists to know R or Matlab: they are now increasingly expected to know how to abstract their work using programming languages such as Python or Spark and their respective ML libraries, which is quite different from running algorithms in a spreadsheet.
Companies want ML data pipelines that run in an infinite loop and generate insights on how to adjust pricing, product mix, etc. It's no longer enough to create graphs or print out models and hand them over to business managers.
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Machine Learning Specialist Salary
Thus, data scientists are becoming more valuable to businesses. So how much can a data scientist make?
Salaries vary by location: Data scientists and programmers of all kinds earn much more in the US than in India, and salaries in the San Francisco area are much higher than in other parts of the US.
Based on published surveys, something can be said about average salaries.
According to Datajobs, data scientists, including machine learning specialists, can earn anywhere between $85,000 and $170,000 a year. Glassdoor lists average salaries at various companies, with Airbnb earning $123,724 and Twitter earning $135,402.
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Where to find work as a machine learning expert
Most data scientists and machine learning specialists find work on popular job portals like Indeed.com, Glassdoor, Monster, Dice, etc. Microsoft lists machine learning related jobs here, and to get into this field, you can look for individual contracts on Upwork.
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Best companies to work for if you are a machine learning expert
There are two types of companies to consider when looking for machine learning jobs: large, established companies and startups. Some companies are primarily data science-focused, while others have a data science department. Additionally, there are two markets for machine learning professionals: clouds where customers can upload data and logs for analysis, and companies that provide APIs and other tools to allow customers to create their own algorithms.
Most startups with machine learning experts are based in California. Some, like Hydrosphere, are based there but have developers abroad. You need to speak Russian to work at Hydrosphere. Companies that work with large datasets and have machine learning departments, like Bank of America and Accenture, are also good choices.
Some of the big players dominating the data science cloud business in the US are Databricks and IBM Watson Analytics. Google has Google Prediction API. If you can't get a job at Google, you can also browse through their list of partners and apply to one of them.
This means that the rise of big data is creating a surge in demand for data scientists. Our high-quality AI and ML courses teach the fundamentals through industry mentorship and course content from some of the most influential global teams so you're industry-ready when you graduate.
You can also partner with IBM's Purdue University for an AI and Machine Learning certification course, which will give you in-depth knowledge of Python, deep learning using Tensor Flow, natural language processing, speech recognition, computer vision, and reinforcement learning.
