Different type of roles in AI Industry Myths related Data science , ML engineer , Data engineer etc.
By: Jeel Gondaliya
Tags: Programming, Machine L, Skills, Deep Learning, Data Science

As a beginner there are many Question in mind related like What is the difference between Data scientist and Machine learning engineer? or What I want to be like Data analyst or Data scientist or Machine learning engineer?

After reading this blog you will have clear decision in your mind which role is suited for you.

Recently I find a report on AI career path by Workera.ai it really helped me to understand different roles in AI Industry. This blog is some kind of conclusion of it and some other research from various forms and blogs.

First thing let’s understand using a AI project life cycle.

Data engineering: All the data manage and maintained by Data engineers and other changes in data so that data can be use for Model development and analysis.

Modelling: All kind of model building ,model tuning and things needs be done before put it to production for users.

Deployment: Created model will use for end user by a platform website software anything.

Business Analysis: All business values check and analysis happens for a product , feature etc. This part company makes decision about product weather to repeat cycle or what changes needed?

AI Infrastructure: All resource required for upper cycle manage by this peoples and end product resources also.

What do you want to say?

Upper cycle is manage by a team and different peoples of team do different work A single person can’t do all the task for industry level project.

Different roles in a AI projects

Data scientist

A person who all the Data engineering , Modeling and Business analysis task .

, and task Data scientist collect / prepare data and perform data cleaning on it then use that data for creating Machine learning and deep learning model. Data scientists don’t do Deployment they will preform business analysis after completed the deployment.

Machine learning engineer

Data scientist and ML engineer do almost same job but ML engineers do deployment also but don’t do business analysis.

Data analyst

They perform data engineering task and Business analysis tasks most for the work of a Data analyst to perform data analysis and helps team and company to make better decision.

Software Engineer Machine learning

A person who all these both skills software engineer and Machine learning not expert level but some understanding various ML concepts will become a better candidate than a software engineer for deployment task and data engineering task.

Machine learning research

A person who done all the task of Machine learning engineer instead of Deployment because there isn’t requirement in research.

There are some variation in Machine learning engineer and researcher like Deep learning engineer and researcher.

Skills require for theses different roles

Data scientist

Modeling in Python using packages such as numpy, scikit-learn, TensorFlow, and PyTorch

in Python using packages such as and Data engineering in Python and/or SQL or other domain-specific query languages

or other domain-specific query languages Business analysis in Python, R, other domain-specific tools such as Tableau or Excel , or presentation software applications such as PowerPoint or Keynote

in Python, R, other domain-specific tools such as or , or presentation software applications such as PowerPoint or Keynote Collaboration and workflow using a version control system such as Git, Subversion, or Mercurial along with a command line interface (CLI) such as Unix and an integrated development environment (IDE) such as Jupyter Notebook or Sublime.

Data analyst

Data engineering in Python and/or SQL or other

domain-specific query languages

in Python and/or SQL or other domain-specific query languages Business analysis in Python, R, other domain-specific tools such as Tableau and Excel, presentation software applications such as PowerPoint and Keynote, and external software services for A/B testing.

Machine learning engineer

Data Engineering in Python and/or SQL or other domain-specific query languages.

in Python and/or SQL or other domain-specific query languages. Modeling in Python using packages such as numpy,scikit-learn, TensorFlow, and PyTorch.

in Python using packages such as numpy,scikit-learn, TensorFlow, and PyTorch. Deployment using an object-oriented programming language (such as Python , and Java , C++ ,) and cloud technologies such as AWS , GCP , and Azure .

using an object-oriented programming language (such as , and , ,) and cloud technologies such as , , and . Collaboration and workflow using a version control system (for instance, Git, Subversion, and Mercurial), a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook, and Sublime, and an issue tracking product like JIRA.

Software engineer Machine learning

Modeling in Python using packages such as numpy,

scikit-learn, TensorFlow, and PyTorch

in Python using packages such as numpy, scikit-learn, TensorFlow, and PyTorch Data engineering in Python and/or SQL (or other

domain-specific query languages)

in Python and/or SQL (or other domain-specific query languages) Deployment and AI infrastructure using an object-oriented programming language such as Python, Java, or C++ and cloud technologies such as

AWS, GCP, or Azure

and AI infrastructure using an object-oriented programming language such as Python, Java, or C++ and cloud technologies such as AWS, GCP, or Azure Collaboration and workflow using a version control system like Git, Subversion, or Mercurial, a command line interface (CLI) like Unix, an integrated development environment (IDE) like Jupyter Notebook or Sublime, and an issue tracking product such as JIRA

Machine learning research

Data Engineering in Python and/or SQL (or other domain-specific query languages)

in Python and/or SQL (or other domain-specific query languages) Modeling in Python using packages such as numpy,scikit-learn, TensorFlow, PyTorch.

in Python using packages such as numpy,scikit-learn, TensorFlow, PyTorch. Collaboration and workflow using a version control system like Git, Subversion, and Mercurial, a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook or Sublime, and an issue tracking product like JIRA.

and using a version control system like Git, Subversion, and Mercurial, a command line interface (CLI) like Unix, an integrated development environment (IDE) such as Jupyter Notebook or Sublime, and an issue tracking product like JIRA. Research by following updates via channels such as Twitter, Reddit, Arxiv, and conferences such as NeurIPS, ICLR, ICML, CVPR, and ACM.

Upper Different roles help you to take decision which roles best for you can you can do progress and develop your skills according to that role and you will also get path for learning skills.

When you have a path way to develop a your skill-set for a particular role you can take right steps towards it.

This blog may help you to find right path for your future journey and Workera.ai report help a lot and I wrote conclusion of full report in this blog you can find full report in the reference.

The world needs engineers and scientists to build the future. In fact, all roles defined in this report are in significant shortage as of 2020. Make your move! — Workera Team

References :

Workera.ai report