The Idea Behind The Full-Stack Data Scientist
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Over the last decade, Data Science has been booming into one of the hottest and most employable professions in the world of science and technology, and for good reason. The amount of data being generated/recorded by businesses, consumers and devices every single day is immense, while the hardware processing power required to analyze such huge amounts of data and extract actionable insights is continually improving in terms of both capacity and cost efficiency. Naturally, that means the only missing part of the puzzle to take advantage of this data-rich environment is skilled professionals who can leverage the resources available to derive value for their organizations. That is why there is such intense demand (and even shortage) for Data Scientists across some of the strongest economies worldwide.
Data Scientists are among the professional roles with the highest growth in demand over the last decade.
The increasing success experienced by businesses relying on Data Science for key insights has, however, also increased the level of expectation on the Data Scientists embedded within the organization. Perhaps because of ever-tightening financial margins (in terms of the salaried headcount the company has to bear) in the current competitive business landscape, or because of the unique all-round software skillset the field’s practitioners possess, Data Scientists are now being expected to shoulder an increasing amount of responsibility in the end-to-end software engineering life-cycle. In other words, Data Scientists are increasingly being expected to take ownership of the entire Data Science pipeline; requiring them to become comfortable with processes upstream to their areas of expertise, such as Data Scraping, Data Engineering, Business Intelligence and Data Visualization, and even with those downstream to their work, such as Deployment of their solutions into a Production-Grade Environment. Simply put, the most valuable Data Scientists are all-rounders who can take full self-ownership of the Data Science lifecycle and require minimal-to-no support from other specialized roles such as Data Engineers or DevOps Specialists. This has given rise to the concept of the “Full-Stack Data Scientist”.
An example of the full Data Science pipeline from IBM. The Full-Stack Data Scientist needs to show ownership across the areas of Data Engineering, Machine Learning and Development Operations.
Full-Stack Data Scientists represent enormous value to their employers for obvious reasons. They effectively handle the responsibilities of three different roles (and possibly a higher number of employees) in one singular profile, giving the organization significant monetary benefit in terms of salaried headcount and also saving the time that would’ve been necessary for back-and-forth integration and division of responsibilities among multiple employees. They represent an excellent starting point for companies that do not have a dedicated Data Science team yet and are looking to build up their Data Science pipeline from scratch, due to their knowledge across the whole suite of relevant applications. They represent a single source of information for senior managers and executives in the company about all things Data Science, a hierarchical structure that usually leads to better accountability and transparency. Lastly, they are also capable of immediately working on developing end-to-end solutions for any idea or concept the organization wishes to explore, ensuring a quick feedback cycle of improvement and allowing the business to get a high-quality functional product in minimal time. These represent significant advantages to any organization in multiple areas, and that explains the increasing hiring push today towards the Full-Stack Data Scientist profile.
Full-Stack Data Scientists represent enormous value to their organizations.
Like any all-rounder profile, however, and especially in an area as interdisciplinary as Data Science, becoming a Full-Stack Data Scientist is a significant challenge and requires competency with a wide variety of tools and techniques across Data Science, Business Analysis, Data Engineering and Software Engineering. In succinct terms, “They Code. They Test. They Ship. They Maintain.” That level of comfort across the suite represents a significant learning curve for a junior Data Scientist who wishes to eventually reach a Full-Stack level of knowledge, but also a significant reward, since such professionals tend to be quite rare and of high value to any organization. In a subsequent post, the path towards becoming a Full-Stack Data Scientist and the competencies required across the Data Science suite for this profile will be discussed in further detail.