Different AI job roles explained

Data Scientist vs Data Analyst vs Data Engineer vs others

The 3rd episode of AIQ is out now where we‘ve talked about the difference between different job roles in the AI and Data Science space. The entire podcast is available below

Here is the summarized version:

Host: Hi everyone, welcome to AIQ, the podcast dedicated to answering your questions about data science, machine learning, and artificial intelligence. Today, we’re diving into the different job roles in the AI domain. You’ve probably heard terms like AI engineer, machine learning engineer, data scientist, data engineer, and data analyst. So, let’s get started with our first question: What are the different job roles in AI and data science, and why is it important to understand the differences between them?

Guest: Hi, thanks for having me. It’s crucial, especially for freshers, to understand these job roles so they can decide which domain to pursue. There are many job roles in AI, like ML engineer, data scientist, data engineer, data analyst, business analyst, deep learning engineer, and NLP engineer. With the ever-evolving AI field, new roles keep emerging. But most of these roles can be grouped into five main ones: data scientist, ML engineer, data engineer, data analyst, and business analyst. We’ll focus on these and discuss their specifics.

Host: Great! Let’s start with the basics and move to the more advanced roles. Can you explain these roles using an analogy?

Guest: Sure, let’s use a cricket team as an analogy. Imagine the team is a company:

• Data Analyst: Analyzes past match data to find insights on player and team performance. For example, they might discover the team performs poorly in the subcontinent but excels elsewhere.
• Data Scientist: Builds models using that data to predict the best outcomes, like selecting the best 11 players for a match based on past performance.
• Data Engineer: Manages the data infrastructure, ensuring all the data from matches and player performance is collected and stored properly.
• Business Analyst: Acts like the team’s general manager, focusing on revenue and operations, such as determining the best time for press conferences or practice sessions.

Host: Interesting! Can a data analyst become a data scientist? And what about transitioning between other roles?

Guest: Absolutely, a data analyst can become a data scientist. Data analysts analyze data and provide insights, while data scientists go further by predicting future trends. For someone starting as a data analyst, transitioning to a data scientist role is a natural progression. Similarly, those in software development roles can move into data-focused jobs by figuring out what they enjoy, be it data analysis, building machine learning models, or something else.

Host: For freshers, which role should they target first?

Guest: For beginners, the role of a data analyst is the easiest to get into. It requires less specialized knowledge compared to roles like data scientist or ML engineer. Once you have some experience as a data analyst, you can think about moving to more advanced roles.

Host: That’s very informative. Thank you for explaining these roles and the pathways to transition between them. That’s all for this episode. We hope our listeners now have a better understanding of the different roles in AI and data science, and how to get started. See you in the next episode!

Guest: Thank you!