There are 5 kinds of data scientists. Which one are you?
By: Mohammad Shahebaz
Tags: Data, Career Advice, Career Paths, Data Scientist, Hiring

As companies begin to understand data science product life cycles for their business processes, the expectations of these role and responsibilities are dynamically changing.

Often I hear, I am a data scientist too. But, how is that my job entirely different from the other person who holds the same title?

Let’s debunk the roles and name them out!

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One of the things I love absolutely about this profession is that one can’t just simply be a statistician, a quick programmer, or an experiment modeller. The role requires multiple technical and soft skills while being bias to the action. That is easier said than done - yet, this is exactly what companies are seemingly exploiting, more so unwittingly.

Here is what one could be:

The jack of all trades and master of one!

What companies who have no data science roadmaps want:

The jack of all trades and master of ALL!

There are FIVE kinds of data scientist roles I have categorised based on my past experiences, interviews & offers.

Hacker Data Scientist Analyst Data Scientist Research Scientist Machine Learning Engineer Customer Data Scientist

And, for the mercy of sciences, there is NO WAY a single person is fit for all kinds of roles.

Let’s begin diving into each one of them.

Hacker Data Scientist

The hacker ones are usually required in startup companies. Usually, they look for the following skills:

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Skills

API Web Development: Flask, RestFul, Computer Networking Data Engineering: SQL, MongoDB, Pandas Data Modelling: Tensorflow, Pytorch, OpenCV Cloud: AWS, EC2, Google Compute Engine, Docker

As by definition, you know what a hacker is and it might be evident how these skills are valued in a startup environment where there are a lot of hats to be worn. They are hacker scientists spending their effort in data sciences and its pipeline end to end, from more of a ad-hoc point-of-view. And, trust me they enjoy it to their fullest!

Career Path

Data Scientist → Senior Data Scientist → Lead Data Scientist →Chief Data Scientist → Head of Data Science

Analyst Data Scientist

This role is more about analysing big-ish data and insights from that data, rather than a core focus on modelling. These roles are something you will love, if you are more interested in finding the answers and exploring depths of data analytics. There might be little to no modelling in a few roles.

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Skills

Data Analysis: Tableau; Power BI; Pandas Data Engineering: SQL, Postgres; Hive; PySpark Data Modelling: Scikit-learn Data Presentation: PowerPoint, Dashboards, Excel

Career Path

Data Analyst → Senior Data Analyst→ Analytics Lead→ Head Analytics → Chief Data Officer

Research Scientist

Data Scientist is more of a professional role, catering to business solutions and needs. But, if you are looking forward to bringing your craziest AI algorithm ideas to life and in general want to push boundaries, you are probably into research.

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Research Data Scientists dive deep into algorithms, create new ones, publish papers, perhaps do a PhD, escape the hype and start creating it instead!

Skills

Data Fundamentals: Statistics, Probability, Calculus, Geometry Portfolio: Papers, Publications and Conferences Research: Depth and Breadth of ML/DL Algorithms is a must

Career Path

Research Associate→ Research Scientist→ Senior Researcher → Head of Research

Machine Learning Engineer

Most commonly known as Software Engineering in Machine learning. The role is more inclined towards deployment and delivering scalable machine learning features.

Skills

Must Fundamentals: Data Structures, Algorithms Data Modelling: Working of internal ML/DL algorithms Execution: Able to code/optimize ML algorithms with ease

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Career Path

Machine Learning Engineer → SDE II → SDE III → Data Science Manager → Director Engineering

Customer Data Scientist

A customer data scientist perfects in marketing the art of data sciences and is able to successfully translate the capabilities into the business conversation and is able to guide the clients to understand what they really need.

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In order to deliver AI as a value addition, one needs to understand not only the algorithm but also the feasibility and the viability, making businesses appreciative of what AI products can deliver and understand the market fit.

Skills

Data Analysis: Tableau, Pandas, SQL Business Intelligence: Soft skills, Negotiation skills Sales & Marketing: Targeting, Chasing Lead, and Retention

Career Path

This field is relatively new, so I haven’t seen many career paths in this front. But, the role is definitely leaning towards the business side.

Customer Data Scientist → Associate Consultant → Senior Consultant → Director Sales/Marketing

Conclusion

As you see, there is so much more nuance and complications to a title one would broadly categorise as “data scientist”. So, for all those who are looking for the rockstar data scientists — Don’t, because they don’t exist.

While some people may claim to be they are or they’ve found one — that’s great! A little delusion can be healthy.

Data scientists, coming from various backgrounds, who worked up this exciting trade of our times, teaching those machines to learn on their own while specializing in the stuff they all love - are already rockstar enough.

What next?

Hope this article helped you understand the industry expectations and kinds of data scientists there are out in the open.

Let me know, if you are an aspiring data scientist or looking forward to making a transition in the career. Feel free to ping me on my DMs, I will make sure to reply to all the DMs. Keep it going. And remember,

You are already a rockstar!