When building a new (physical, literal) house, a team with extremely differentiated skills are working together to create something new. The masons lay bricks, the roofers keep the house sheltered, painters decorate walls, landscapers beautify a garden etc.

Although each of these personas may be able to do what the others do at a stretch, they are categorically different.

Just like building a house, every data organisation functions as a team with diverse skillsets. There are three major categories, with overlapping skillsets in the field of Data Science.

A big disclaimer here is that these categories are not fixed and may vary depending on the company and the project.

Three categories of Data Jobs

Data Scientist

In the nuanced realm of data exploration, the data scientist seamlessly integrates statistical analysis and machine learning proficiency to enhance decision making where there is uncertainty.

Mathematicians

If tackling multicollinearity challenges and unravelling mysteries of Bayesian inferencing keeps you up at night, then a job as a data scientist may be right for you.

The mathematician persona is addicted to solving complex problems with mathematical models that help predict outcomes with varying levels of certainty. This allows others to extrapolate on the model to turn uncertainty into actionable business-decisions

The data scientist uses a wide array of mathematical and statistical tools to optimise models, squeezing more accuracy performance out of models. This role could be a playgroud for the mathematician persona

Skills

• EDA
• Mathematics
• Probability and Statistics
• Machine Learning
• Programming

[[The Comprehensive Data Scientist Roadmap]]

Data Engineer

As a master craftsman of data infrastructure, the data engineer strategically engineers robust systems, orchestrates pipelines and automating flows to seamlessly productionise models.

Architect

If orchestrating kubernetes clusters and scheduling DAGs makes you feel like a puppet master, then a data engineer is the right role for you.

The Architect persona cares about the system and how to keep it robust, clean and easily moddable. Architectural and systematic design choices as well as automation are inherent in a Builder’s thought process.

The data engineer ensures that the infrastructure on which data resides is in smooth operational order, and an individual with the Architect persona would fit in perfectly.

Skills

• DevOps
• Programming
• Database management
• Data modelling
• Cloud platforms
• Version control systems

[[The Comprehensive Data Engineer Roadmap]]

Data Analyst

As a proficient data interpreter, the DA navigates the intricate landscape of datasets with finesse, uncovering valuable insights that help drive the decision-making process.

Commercially-savvy

The commercially-savvy are well connected in their domains, and have good understanding of the important metrics in their field. For instance, a marketing specialist would know how to derive the best cost per acquisition (CPA) metrics, while a financial analyst needs to understand the return on investment (ROI), just as supply-chain specialists can measure inventory turnover etc.

The Data Analyst is a commercially-savvy individual who excels at manipulating data in the best possible way to present visually engaging metrics to aid in decision making.

Skills

• EDA
• SQL
• Data visualisation
• Domain expertise
• Statistical Analysis
• Programming and Scripting

[[The Comprehensive Data Analyst Roadmap]]

Categorically not empirical

There are no hard lines between the above categories. More often than not, each role often requires us to branch into the other categories when solving problems.

Even the same job title may differ in responsibilities between different departments of the same company, let alone different companies.

However, understanding the specialisms and the dynamics between them helps you understand the kind of work you would like to do.

This diagram roughly estimates what each job title would look like based on which camp they are most likely to fall into. Once again, your mileage may vary!

Choosing your career path

“To know yourself is the beginning of all wisdom.”

– Aristotle

When you understand what you like and what you don’t, you may naturally have an affinity for the 3 different personas above: The Mathematician, The Architect, and The Commercially-savvy.

From there, you can select the category of data jobs that help you decide on your career choices.

What matters ultimately is building your skills and finding a role that allows you to apply what you know and make an impact.

Datalytes helps you showcase your skills more accurately to potential employers and assists you in choosing where you’d like your impact to be

This article is written based on our view of the industry, we’d love to know your thoughts in the comments below on how you’d categorise differently!