3 Weeks Beginners Guide to Ace Data Science Interview: #Day 3
Understanding different roles & skills of data scientist
About the Series
Data Science field is an exciting career choice and seeing a lot of hiring across fresh, lateral and experienced job positions. It’s one thing to know the concepts and totally another to crack the rigorous interviews for data science positions. If a candidate is aware of the different questions and the interview process, he is on the right path to an excellent career in the evolving Data Science field.
This 3-week beginners guide to Ace Data Science Interview will be a useful asset for individuals who are preparing for the Data Science interviews. Every day for the next 21 days, we will talk about the different areas of the Data Science field and cover them elaborately. So sit back and start reading the article to get a finer understanding of the Data Science field and go prepared for the interviews.
However, the reality is most of these jobs are for those who already have experience. Entry-level data science jobs, on the other hand, are extremely competitive due to the supply/demand dynamics. Data scientists come from all kinds of backgrounds, ranging from social sciences to traditional computer science backgrounds. Many people also see data science as a chance to rebrand themselves which results in a huge influx of people looking to land their first role.
So before starting to search for a role, it’s important to determine what flavor of data science appeals to you. Let’s first get our facts clear, who are the people this complete guide is designed for.
Different industrial roles of a data scientist:
To understand each role let’s dive into the pyramidal hierarchy of different data science roles.
(1) CxO Roles or Senior Executive:(set direction):
These are the people who sit on the top of the pyramid, without these guys permissions any of the data science work has to be done in the organization can’t proceed further.
(2) Mid Management Roles(execute the projects and establish priorities):
The middle rung in the pyramid covers the mid-management roles in a DataScience team. And help to efficiently flourish the data science projects.
These roles include
- Team Lead
- Manager
- Project Managers
- etc
(3) Individual Contributors:(performs magic):
Though they are at the bottom of this pyramidal structure, these are the people who perform the real magic. These roles are what we have been primarily hearing about in the industry.
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineers
- etc
These guys are the heart of a data science project and their workflow aspects are majorly technical.
Roles under consideration while designing this 3-week series.
Considering the interdisciplinary nature of the data science field, this series brings together researchers, practitioners, and instructors from statistics, computer science, machine learning, and analytics.
So this 3 weeks series is designed for the people who lie in the bottom section of the pyramidal hierarchy and those are the Individual Contributors(True Musicians).
Though this series my sole intention is to share my learning with others and help out every practitioner in their interview process so they can hit the Bull’s eye of this Data Industry.
Different job titles that fall under the Data Scientist Belt:
- Product Data Scientist:
Analyze user data and extract insights that are consumed by product managers.
- Machine Learning Engineer:
Builds, tunes, maintains and improves custom machine learning algorithms.
- Decision Scientist:
Uses data analysis and ML techniques to improve a business process.
- Data Scientist Consultant:
Enables clients to practice/execute data science .
Enables teams to figure out how to practice/ execute data science better.
- Data Engineer:
Builds data pipelines and is responsible for Machine learning in the production system.
Did you get confused with all these job titles? If you are not able to decide as to which job roles suit you, focus on what you want, your strengths and where you fit in rather than the different Data Science jobs.
Why Data Science Interview process is so complex?
From the above image, you can understand the variety blend of questions a data scientist can expect in the interview process and that’s what makes this field so complex. The bar is pretty high; you need to prepare for every possible question that can be asked and what’s better than a compilation guide for your preparation!
How to get the best out of this series:
Setting Goals Isn’t Enough: Setting Daily Intentions Will Change Your Life
Voicing intentions will take your mind off of your problems and perceived limitations. Instead, it will place your focus on something that will positively impact your life. Clearly stating how you intend to feel today instead of wishing that you felt better puts the power of change in your own hands.
Write down your goal and try to achieve them with 100% intent.
If this blog helped you in any way, then do Follow and Clap👏, because your encouragement catalyzes inspiration and helps to create more cool stuff like this.
Check what’s on Day1, and Day2.
Sources: https://towardsdatascience.com/mastering-the-data-science-interview-15f9c0a558a7?gi=ee958ba99877
Final Thoughts and Closing Comments
There are some vital points many people fail to understand while they pursue their Data Science or AI journey. If you are one of them and looking for a way to counterbalance these cons, check out the certification programs provided by INSAID on their website. If you liked this story, I recommend you to go with the Global Certificate in Data Science & AI because this one will cover your foundations, machine learning algorithms, and deep neural networks (basic to advance).
