What does it take to be a Data Scientist?

Data Science is still a blue skies phenomenon, despite the momentum it has gained in recent years. The “catch-all” term doesn’t offer specifics about the skill sets needed and the measures for success. Perspectives differ on where the boundaries of its definition lie – job roles span like a web-tree of branches that include data analyst, data engineer, data architect, data scientists, and more recent trending terms like machine learning engineer and artificial intelligence scientist. In the course of my career, many have asked what does it take to be a Data Scientist, and more importantly, what does it take to be exceptional in the field?

A Data Science professional requires a combination of statistics and computing knowledge as baseline skillsets. Ideating is easy, but execution and experimentation is key. After all, Data Science is an applied subject. The sooner you execute, the more room you have for iterating and validating models to achieve robustness.

From a technical perspective, the Data Science profession demands excellence in two areas. First, the real value of data science lies in developing predictive models with accuracy. Data Scientists need to deftly calibrate their training models and strike a balance in how they reduce bias in their model at the expense of increasing variance. Well thought-out models take a methodical approach to ensure that each variable is given due and rigorous consideration, especially in the context of its business.

Second, orchestrating big data platforms aimed at scale is essential as this sets the foundations for meaningful data analytics and visualizations. Truth be told, companies struggle with developing data infrastructures that accommodate volume, versatility and the veracity of data. Having the know-how to manage big data, while navigating through corporate information security policies and regulations, makes a Data Scientist valuable.

While mastery of technical skill is necessary, making your mark in this profession requires finesse. While Data Scientists’ skills are transferable across industries, I cannot emphasize enough on the need for Data Scientists to build an intimate understanding of the industry they work in. We live in a less than perfect world, where each industry has a unique set of challenges that arise from inter-dynamics among stakeholders, regulatory policies, gaps in asymmetric information and even data transparency. The more you understand the nuances of the industry, the better equipped you are at finding quantitative challenges to resolve that are relevant and impactful to the industry’s stakeholders.

The professional field of data science has cemented its relevance across industries with many corporates setting up their own in-house data science teams. In the years ahead, the data science profession will continue to evolve with new roles emerging and other becoming commoditized. Whatever shape and form this profession will take, it is important to get the basics right, so that the benefits of data science can be significantly reaped.

This article was first published on APAC CIO Advisor, Learn@IBF and Curafy.ai/blog.