Full Stack Data Scientist — A Myth or the New Normal in 2023
Introduction
Data science is so far the most eye-catching field, and data scientists’ job is still the sexiest. As the usefulness of AI and ML proliferates with the new implementation and research verticals, the demand for data scientists is also growing. But the broad universe of data-driven technology brings takes us to a new horizon of data science job roles — the full-stack data scientist’s job role. So, now the question is whether anything of such sort exists. Are full-stack data scientists an actual profile companies are looking forward to or a myth? Again, whether it’s going to be the new normal for 2022 and the years to come? This article is nothing less than a myth buster and will make the readers witness the future data science profile and what companies are looking forward to in this job profile.
What does the term “Full-stack” mean?
Full-stack refers to the skills & technologies an individual should possess to complete the entire project. Here each technology or component of the project is called the stack. In other words, they are professionals who have the experience and expertise to develop a project from start to end, leveraging different types of technologies and techniques. Today, full-stack web developers, full-stack engineers, and full-stack Dev-Ops professionals are some well-known job profiles that companies want to market the products faster. Full-stack data scientist profiles are also becoming a buzzword and gaining popularity. Of course, it is not a myth but will soon become the new normal. It is a next-level data scientist’s job from whom companies are expecting more than usual data scientists’ role. Let’s explore who full-stack data scientists are.
Who are full-stack data scientists?
Approximately 10–12 years ago, when data science came up to a new horizon, large companies like Google, Microsoft, Amazon, etc., hired data scientists. Apart from data scientists responsible for analyzing data & model development, these companies also hired data architecture, BI analysts, data mining professionals, ML engineers, AI-based product developers, analytics teams, etc. They all help the company support advanced data science operations. Companies hire data scientists because they have a specialization in analyzing and understanding data and can provide the best datasets to train ML models efficiently. But full-stack data scientists have much more responsibility than simply interpreting the best data & modelling the algorithms with efficient datasets.
Although there isn’t any ideal definition of full-stack data scientists, it can be explained descriptively. Full-stack data scientists (FSDS) are those professionals who come with multi-dimensional viewpoints. They are responsible for analyzing large structured and unstructured datasets to extract knowledge and insight from data and implement it in various models and projects. Apart from basic data scientists’ responsibility, they also deploy AI & ML models and integrate different business applications in projects. They can also develop APIs that leverage complex datasets from cloud storage & understand business needs from applications or APIs for generating better Return on Investment (ROI). A single person can do all the work right from data wrangling & gathering to developing & deploying APIs and ML-driven projects. They cater to multiple expertise such as data analysis, web development, business analytics, sound knowledge of AI & ML, REST API development and deployment, and understanding of the company’s business angle. Apart from all these technical skills, they have breadth-taking analytical and statistical proficiency. They know which data will bring the best business growth and development.
Characteristics of full-stack data scientists –
Here are some one-liners that a full-stack data scientist delivers to a company and is accountable for in a company.
· They cater to a massive business impact because large projects entirely depend on their skills & craftsmanship.
· They are responsible for generating and extracting a high influx of data that will later help model training.
· They cater to cutting-edge development skills in various domains (web, AI, API, etc.)
· Through them, companies can adopt the complete AI lifecycle & leverage their skills toward data-driven product automation
· They have very high job satisfaction.
· Since they render the tasks of multiple profiles with lots of responsibilities, the full-stack data scientist is one of the highest-paid profiles in the industry.
But the benefit of such a profile comes with some imperfections and challenges.
Imperfections and challenges
Due to some imperfections and challenges, many people consider this profile a hoax or myth. Yes, thinking from these perspectives is logical. These are:
· The full-stack data scientist’s profile is a multitude of different skills. So, who should interview these candidates to assess these dimensions and their connectivity?
· How much business knowledge, software development skills and production deployment proficiency do companies expect from a full-stack data scientist?
· Where do they fit in the company?
· Are they jack of all trades and masters of none?
But, with time, full-stack data scientists will overcome these challenges as well.
What can a business do after hiring a full-stack data scientist?
The trend toward full-stack data scientists is booming, and companies are looking forward to leveraging such potential employees. Companies like Amazon and Google have started hiring or promoting data scientists and product managers to full-stack data scientists’ roles. Companies can depend mostly on these full-stack data scientists to not only build a full-scale application lifecycle but can make money through AI and data-driven automation.
Companies can also create full-stack data science teams with a lifecycle view. Full-stack data scientists can also manage projects entirely and work as project managers as they have multiple proficiency. Full-stack data scientists are also responsible for developing complete AutoML systems & REST-based model development. That makes it easier for the company plus the development & data science team to deliver robust AI products. Because of full-stack data scientists, the time to market a product will reduce by a significant level.
Wrapping Up –
Businesses are leaping to a new horizon where AI, ML, and data-driven products will lead the market. So, companies will require the potential of full-stack software developers and data scientists together into one superhero. Therefore, full-stack data scientists will be the new normal in the years to come.
