What career paths are available in AI

Types of AI roles. What does each role entail. Career path for each role.

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They say 2020s is going to be the decade of AI. It’s a fantastic field to be in right now. There's a lot of demand for AI talent and it’s only going to go up from here.

If you're a professional who wants to enter AI, there are many potential paths you can take. Just look at the sheer number of job openings! But what are the types of roles available in AI? What’s the career path for each type of role?

In tech companies, the career path usually looks like this:

Individual contributor
Manager
Director
VP
C-level

As the company gets bigger, you might see a few more levels such as the role of Senior Director between Director and VP levels. But overall, this is the trajectory you will take to build your career.

In terms of the work you'll have to do, there are 5 types of roles available in AI through which you can can enter the field:

• Data Analyst

• Data Scientist

• Data Engineer

• Machine Learning Engineer

• AI Research Scientist

Each role has its own career path. Here's what you’ll have to do within each role:

Data Analyst

Data Analysts deal with analyzing raw data. There's usually no structure, labels, or explanations. You have to deal with a lot of unstructured data.

Skills needed: SQL, Excel, Python

Common scenario for a Data Analyst: You receive data from a customer or another team member. You have to figure out what it means. You have to structure it. You don't have to use Machine Learning algorithms. You have to send the structured data back.

Here are a few examples of data analyst roles:

• Financial Analysts: They look at financial data and identify revenue/investment opportunities

• Marketing Analysts: They look at market trends to determine what customers to target

• Business Intelligence Analysts: They look at an organization's IT processes and structures to make them more efficient

• Healthcare Analysts: They look at health records to improve the quality of care

Even though job titles vary, the core component of the role remains the same:

Translating raw data into human-readable business insights

Here’s the career path:

Data Analyst
Manager, Data Analytics
Director, Data Analytics
VP, Data Analytics
Chief Data Officer

Data Scientist

Data Scientists take the structured data from data analysts and try to model it. They’ll see how to solve a given problem using the available data.

If the goal is to predict pump failures, they will figure out what algorithms they can apply to make this prediction. If the goal is to find the meaning of a sentence, they will figure out what algorithms they can apply to achieve this.

The outcome of their work is a model that can analyze unknown data in the future and provide an output that can be used by people for their tasks

Their work is usually ad hoc. They don't usually deploy these models into production. They solve problems and write code to build the models. The good ones build models such that they can be wrapped into production modules easily. It’s a good skill to have.

Skills needed: AI algorithms, Python, libraries, tools to parse data

Common scenario for a Data Scientist: Data analyst sends structured data to you. You have check which model works for this data. Build out the model. Refine it until the accuracy is high enough. Run it on test data to confirm.

Here’s the career path:

Data Scientist
Manager, Data Science
Director, Data Science
VP, Data Science
Chief Data Officer

Machine Learning Engineer

ML Engineers deploy models to production. They’re responsible for making the system (e.g. cloud platform, mobile app, API service) work in the real world. Their job is to make sure that:

• The models remain up to date as new data comes in

• The models don't become slow or bulky over time

• The models can handle increasing volumes of data over time

• The models can concurrently handle many users at a time

Skills needed: Machine Learning libraries, algorithms, Python, databases, APIs, cloud deployment tools, devops tools, tools to parse data

Common scenario for an ML Engineer: Data scientist sends model files. You have to wrap it into a module. You have to deploy this module into production. You have to load test it to see if it breaks. You have to automate the model retraining process when new data comes in.

If it feels like a lot, you’re right about that. Good ML Engineers can command a lot in the market.

Here’s the career path:

ML Engineer
Manager, ML Engineering
Director, ML Engineering
VP, ML Engineering
Chief Technology Officer

Data Engineer

A Data Engineer’s job is to make sure the data is being gathered, stored, and retrieved in the most efficient manner. They choose the right underlying infrastructure for manage data. They deal with tools that handle large volumes of data efficiently.

Skills needed: databases, data streaming, APIs, python, devops tools, tools to parse data

Common scenario for a Data Engineer: ML Engineer deploys the modules. You have to make sure data flows in and out of your platform correctly. You need to store/retrieve the enormous quantities of raw and processed data. You need to make it fast.

This is a very engineering heavy role. World’s largest companies need good data engineering to run efficiently. Data Engineers command a premium as well.

Here’s the career path:

Data Engineer
Manager, Data Engineering
Director, Data Engineering
VP, Data Infrastructure
Chief Technology Officer

AI Research Scientist

An AI Research Scientist’s job is to work on new algorithms. It's a slower role as compared to the others. But it’s prestigious at the same time.

Your work may not go into production as frequently as the other roles, but you will publish many research papers. It’s good for folks who enjoy academic research.

Skills needed: algorithms, Python, tools to parse data, Machine Learning libraries, having a Masters or PhD helps, you need to write well

Common scenario for an AI Research Scientist: Experiment with new algorithms. See what works and what doesn't. Your work is not usually meant for production environments. You'll publish many research papers.

Here’s the career path:

Research Scientist
Manager, R&D
Director, R&D
VP, R&D
Chief Data Officer

Where to go from here

Once you determine what role you would enjoy, you can get started on building a profile in that direction. It’s important to develop skills that will help you stand out. The more you specialize in a particular role, the more premium you can command for your skillset in the market.