Explore Basic Concepts of a Data Scientist role

Data science doesn't always have to mean creating complex models. So, if it's not that, what is it exactly?

Published: 18 October 2022

In this article, we will guide you through the magic of data science. By doing so, we will take a deep dive into what it means to be a data scientist, including:

  • The added value of Data Science 
  • The responsibilities of a Data Scientist 
  • The toolbox of a Data Scientist 

Intercept has guidelines that assist you in determining your data journey. Click here for a description of our Data Life Cycle Management.


The added value of Data Science

At Intercept, we believe a data scientist should use data to create models that gain information and offer insights. These insights can be used to act upon, helping you to make better business decisions.  

Let’s consider a data science example: 

Some banking applications bucket your purchases into categories such as groceries, utilities, and transport. These results are then displayed on the bank application on your phone. Nowadays, these applications can even show predictions of spend per category or forecast upcoming transactions.  


It’s easy to take this functionality for granted. However, it is good to note that this is only possible using data science and building an intelligent system.

The responsibilities of a data scientist

At Intercept, we believe some high-level responsibilities of a data scientist are to: 

  • Choose the tools that make up the data science environment;
  • Have a thorough understanding of the developed models;
  • Create secure data science infrastructure, data, and models;
  • Inform stakeholders about your developed and deployed models.

It all starts with choosing the data science environment you will use to develop and test your models and selecting the production environment. At Intercept, we believe Azure Machine Learning as an Azure service is a great environment to work and collaborate.

Of course, the primary responsibility of a data scientist is to create data science models that create actionable insights. However, it might be even more important that a data scientist thoroughly understands the algorithms and frameworks used. For example: If you investigate time series and want to forecast sales demand, you should explore the inner workings of multivariate regressions.  

A data scientist also must consider the security of the data science solution. Securing your data science models and environments means protecting yourself against attacks.

Shared responsibilties

Some of the responsibilities mentioned above are shared responsibilities. For example, networking security should be aligned with your infrastructure employee. Meanwhile, the overall security of your data science solution should be aligned with the security officer. Hence, informing all stakeholders about your developed and deployed models is essential. Not only should outcomes be reported, but data lineage, training sets, or flag models should also be reported as sensitive when biased.  

At Intercept, we can help you identify those responsibilities, ensuring all responsibilities are covered, or we will do it for you! 

Are you interested in knowing more about security? An article on this topic will be released soon. Stay tuned for more content on Demystifying data science! 


The toolbox of a Data Scientist

Intercept believes that to start a data science project, it comes in handy to have sufficient knowledge on: 

  • Programming Language;
  • Data Science environment. 

Knowing a programming language enables a data scientist to understand written code in multiple languages. It also influences your choice of data science platform. At Intercept, we use Python, a stable language with a solid user base. In addition, it has lots of machine learning functionalities and integrations with data science tools.  

Several AI services available within the Microsoft Azure platform can be a building block in your AI practice. At Intercept, we focus primarily on Azure Machine Learning as the platform for your data science projects which serves Python, and for easy drag and drop environment of algorithms such as prediction, it has ML Studio built-in.  

However, in the case of pre-built algorithms specific to a function, we can use other services like Azure Cognitive Services and Azure Bot Service

At Intercept, we believe it’s super important for data scientists to comprehensively understand the tools in their toolbox to tackle business challenges using data science as efficiently as possible. Don’t know where to start. We can tailor your toolbox based on your use case while guiding you through each step.  


How does Intercept tackle Data Science use cases?

Following the DLM approach, Intercept tackles your Data Science project step by step. Together with our Data Scientist, we can guide you through your business requirements, translating them into a Data Design. This Data Design acts as the blueprint for your Data Science project. Schedule a meeting with us to identify what challenges you’d like to solve with Data Science.