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Machine Learning is a part of Artificial Intelligence, systems that actually provide a framework to build sophisticated AI components.

This is the ability of the machine to learn from the instructions provided and past experiences and then solve a problem. Machine learning uses algorithms and statistical models to achieve this feat.

If you are reading this post, chances are you have contemplated a career in Machine Learning and are trying to gather as much information about this as possible.

Machine learning has confusing algorithms… Machine learning has advanced statistics… Machine learning has complex coding… Machine learning is a monster that can’t be tamed… Machine learning is a fad!

Have you ever asked these questions? Okay, maybe you didn’t ask them out loud but you did Google them!!

To help you understand the different roles available in Machine Learning, this post will dive deep into the nuances and particulars of different roles and help you make more informed decisions.

The four roles we will be discussing today are as follows:

  1. Machine Learning Engineer
  2. Data Scientist
  3. Machine learning Scientist
  4. Machine Learning Researcher

Lets begin with the analysis of these roles so you can figure out where you fit in.

 

1. Machine Learning Engineer

 

Machine Learning Engineers are the brains behind complicated machines that learn and perform with minimal human supervision. 

Machine learning Engineers go beyond this and develop programs that let the machine perform actions, even when they are not instructed to do it. 

Here are the tasks that a Machine Learning Engineer typically performs.

– Use a programming language and Machine Learning libraries to test the competency and performance of machine learning algorithms, by conducting different experiments

Code to carry out prospective machine learning solutions

– Improve the system’s performance and scalability

– Ensure smooth data access through database and back-end systems

– Deploy custom-made machine learning codes; codes specific to the system or end goal

– Build use cases for the programs and algorithms you develop

Grouped at the juncture of Data Science and Software Engineering, a highly-skilled Machine Learning Engineer ensures that the raw data is restructured as a Data Science model that can be adjusted as per need. They prepare the production level model to process massive data.

Do you know the most interesting part of their profile?

They build programs and develop algorithms that help a machine to comprehend commands to think on their own. 

Because experimentation is the base of what they do, entrepreneurial instinct and creativity are the basic criteria of a machine learning engineer profile.

 

2. Data Scientist

 

Data Scientist is touted to be the sexiest job in the 21st Century!

An expert in machine learning and statistics, Data Scientists build machine learning models that predict and solve critical business problems. 

What a Data Analyst can do, a Data Scientist can do that and even more. A Data Scientist is adept at cleaning, analyzing and visualizing data-sets and instructs and improves machine learning models.

Here are some important tasks for a Data Scientist:

  • Evaluating statistical models to gauge the validity of the models’ analysis
  • Creating refined predictive algorithms with the use of machine learning
  • Regularly testing and improving the efficiency of machine learning models
  • Using data visualization to summarize advanced analysis

Data scientists garner insights and communicate these findings to the business stakeholders who have a non-technical side.

Picture them as a bridge between the technical and non-technical people. This is why skills like data visualization and storytelling make more sense for them. They design illustrated visualizations and make insights comprehensible by everyone. Data scientists are a combination of data analysts and a step more than that. 

MATLAB, Python, R, SAS and SQL are the languages they use in their daily modus operandi. Python and R are the only tools that a data scientist uses to communicate findings and for other processes.

 

3. Machine Learning Scientist

 

A Machine Learning Scientist is an  expert on using data to training models. 

The models are later used to automate processes like image classification, speech recognition, and market forecasting. 

A Machine Learning Scientist needs the following qualifications:

  • In-depth knowledge on supervised and unsupervised machine learning algorithms including classification, clustering, and regression.
  • Experience building production systems with statistical analysis, data modeling, regression modeling and forecasting, time series analysis, and deep learning neural networks.
  • Expertise in coding using one or more programming languages such as R, Python, MATLAB, and Spark to build machine learning models. Skilled in manipulating and processing data using libraries such as Scikit-learn, Pandas, and NumPy. 
  • Experience processing, filtering, and presenting large quantities of data. Ability to design for performance, scalability, and availability.

A Machine Learning Scientist is a scientist in essence, concerned with building, training and evaluating models end to end.

 

4. Machine Learning Researcher

 

Machine Learning Researchers come often from the academic field and their background is usually in university research projects.

Researchers are very different from actual Machine Learning Engineers who constantly apply and evaluate models in the real-world environment.

Researchers often work alone or in small teams. Researchers have a fixed dataset that they train a model on and once satisfied with the results, they write a paper and often never go back to the code ever again or actually deploy the model at scale for real world use cases.

In theoretical or research settings, repeatability, record keeping, testing and collaboration play a much smaller role compared to production systems.

Machine Learning Researchers is a cerebral profession. They explore the foundations of machine learning. 

This involves a lot of detailed and intricate study into the behavior of machine learning algorithms and learning principles. Fields such as statistical learning theory, algorithmic learning theory, and computational learning theory involve such explorations.

This concludes our list for Top Machine Learning roles.

Data science and AI are the uncrowned kings of futuristic jobs, but you also need to align your skills with the opportunities in this field to make machine learning a profitable investment for yourself!

You have to take stock of developments in the industry before you decide your place in it, or out of it! 

Your professional background isn’t all that relevant in determining whether you would flourish in the field of Machine Learning.

In the end, before you go, take our small Machine Learning Aptitude Test:

Machine Learning Aptitude Test

Ask yourself these questions!

  • Do you have an understanding of the machine learning and industry trends?
  • Do you build on this understanding by extensively reading the news, blogs, books, watching videos and listening to podcasts?
  • Do you know how industries are using machine learning to their advantage?
  • Do you understand how you fit in?
  • Can your skills be polished and matched to a machine learning expert?
  • Do you have an aptitude for learning and applying technical tools on a daily basis?
  • Do you have the foresight to use mathematical and statistical concepts to extract maximum insights from data-sets?
  • Do you have the appetite to constantly learn new concepts and techniques?
  • The industry is fast evolving with newer breakthroughs made every hour.  Are you ready to keep up?
  • Can you place data driven techniques in a business context?

If you are convinced that your aptitude is a good match for a machine learning job, go for it.

 

 

 

 

 

 

 

Author

Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

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