According to the Bureau of Labor Statistics, the employment outlook for data scientists is expanding at 22% in the United States, which is far higher than the average for most other occupations—and that's no wonder, considering the relevance of data in our lives.

Data science is crucial to developing new technologies that provide us insights by processing enormous amounts of data. Data science plays a significant role in the technologies that power social networking, GPS navigation, streaming media, and the internet. Every modern convenience and benefit depends on it, including internet shopping, remote work, and gaming. Due to the high demand for data scientists, many institutes now offer rigorous data science courses in Bangalore, for data science aspirants. 

So how do positions in data science truly work?

Looking at these professions and how they are described at major digital firms like Facebook, Apple, and Google is one way to better understand what data scientists perform.

The "big five" technology businesses in the U.S. with the largest markets and most recognizable brands have an acronym. FAANG, or Facebook, Apple, Amazon, Netflix, and Google, is the acronym for this group. (The market community will soon have to alter this name due to Facebook changing its name to Meta!)

We may learn more about other programs, such as how The Data Incubator (TDI) prepares you for that future, by demonstrating how these large corporations manage data science work.

Data Scientist

The field of data science is expanding. The job title hasn't been around for more than a few decades, just like the technology it drives. There were just 1,700 data scientist roles available in 2016. After six years, the number of job opportunities has increased by almost 480% and is still rising. There will be around 10,000 positions available for data scientists in 2021. Moving in this direction will be delighted to learn that data science occupations offer something even more amazing: high work satisfaction levels! Data scientist is ranked as the third most desired job in America by Glassdoor, with a satisfaction rating of 4.1/5.

Businesses of various sizes employ data scientists. Although FAANG companies—Facebook, Amazon, Apple, Netflix, and Google/Alphabet—might come to mind when thinking about the demand for data scientists, the truth is that data experts are required at businesses of all sizes and across various industries.

To improve their ability to forecast for and about their consumers, businesses ranging from Intel and IBM to retailers and social media platforms are investing in data science.

Careers in Data Science at Small vs. Big Businesses

Knowing what to anticipate while beginning a career in data science might be difficult. While each firm is unique, there are certain overarching differences between smaller and bigger businesses that career professionals should consider. Consider the advantages and disadvantages of working for each type of organization carefully, then use that knowledge to direct your search for your next employment.

Small Businesses

Data science requirements for smaller businesses will differ from those for bigger ones.

Data scientists may be assigned a number of duties relating to manipulating data in various ways when working with startups or smaller teams. Smaller businesses often provide lower wages and fewer benefits, but they frequently encourage a stronger work-life balance, and in certain cases, this flexibility may offset the discrepancy.

For instance, a smaller firm might be able to pay you a bit less than a larger one can, but you might also be able to work from home or have unlimited vacation time. If the business expands, there may also be good opportunities for professional progression. This category typically includes startups.

Large Companies 

More benefits and perks are available in large organizations than in smaller ones, and their basic wages are often larger. But there is a cost involved. The hours can be lengthy, and it could be difficult to establish a work-life balance at first. Nonetheless, some people would rather make that exchange. They often receive greater compensation in return, along with more job role structure.

The tasks and responsibilities of each employee are usually clearly laid out in these large corporations, as are the professional advancement tracks. Bigger businesses could also provide their data science experts with greater resources.

Roles and Duties in Data Science

The job description for a data scientist at a FAANG company is comparable to that of some top businesses. Applicants often hold graduate degrees in STEM fields and have a track record of accomplishments in data mining, data analysis, programming, and machine learning.

A certificate from Learnbay’s data science course in Bangalore can assist you in obtaining the training and experience required to compete in the data science sector through our data scientist programs. 

Data professionals may be qualified for various data science positions at these organizations. The data science positions they provide often fall into one of four categories:

Data Analyst

Data analysts examine data to find areas for improvement. They develop reporting systems and create metrics. In order to draw meaningful conclusions about the trends they find, many data analysts collaborate extensively with other departments, such as business or operations.

Machine Learning Scientist

Roles as machine learning scientists go a little further in their data analysis. In addition to conventional data analysis, they employ machine learning to draw even more conclusions about the significance of the data's trends.

Applied Data Scientist

Applied data scientists draw on machine learning and data analysis to create models and provide scalable solutions. The function of statistics in this is significant. Applicants must also thoroughly grasp automated data systems, data validation, and model creation.

Data Engineer

Engineering data creates tools. They must create infrastructure, pipelines, and data architecture. Data engineering is slightly different from the other data science occupations we have spoken about so far, which all fall under the umbrella of data science. In order to meet these demands, TDI provides a Data Science & Engineering Fellowship focused on these requirements.

Knowing the application procedure

Any FAANG company's application procedure, from Amazon to Google, will be the same. Applicants first submit an online application. Typically, you create a profile that you may use to apply for various jobs. Candidates are urged to reapply and apply to comparable jobs because there are many open positions. At this point in the hiring process, timing is crucial.

You will begin the interview if a team member believes you would be a good fit for the position. Unlike many other fields, the data science interview process can be unpredictable, and it is seldom simple. The organization, the department, and the job will all affect the specific procedure. Before you are called for a more in-depth interview, you may anticipate taking an online exam, participating in a few phone or video talks, and working on a short project.

Often, in-depth interviews (or interviews) occur in person or through video. You could have several interviews on the same day in some circumstances. For instance, Google routinely schedules three to four interviews for potential employees daily. The interviews are quite systematic in an effort to evaluate each candidate equally so that they may be more easily compared. Remember that everyone who makes it to this point is qualified for the position. The concern is whether or not they would be a good addition to that squad in that position.

Workplace Conditions at a Big Data Science Firm

At bigger businesses like FAANG, work environments are frequently divided into smaller teams that are a member of a larger group. A good illustration of this is Amazon. There is a two-pizza limit. The theory is that the most efficient teams are small enough to be satisfied by just two pizzas. The lower group size is intended to encourage participation, but it also has the benefit of having less political intrigue.

With large corporations, finding a work-life balance may be challenging. Teams are under pressure to perform because the stakes are so high. Instead of a balance, Jeff Bezos of Amazon refers to work-life as a circle. At work, individuals are typically content at home, and vice versa. FAANG firms provide all the salary and benefits required for employees to live their best lifestyles while achieving their goals since they know the high standards.

Constantly Continue Studying Data Science for Success

Data science is a fulfilling career, but it doesn't progress like other careers. The field itself is constantly growing. Given this, it stands to reason that the most successful data scientists are those that expand their skill sets and discover novel applications for their abilities. Many of the best professionals in the field of data science acquire the skills they utilize on a daily basis after graduating. 

In order to broaden their professional horizons, often in surprising ways, they decided to study new programming languages and techniques for data modeling. There will be setbacks since no data scientist is an expert in everything. Success is based on a lifelong love of learning.

Become a Data Scientist Now!

The opportunity to work as a data scientist or data engineer has never been greater. Data workers with strong skills are better equipped to give accurate, perceptive, and useful data. Learnbay offers hands-on data science training in Bangalore, where top industry professionals train students on the abilities required to succeed in the data world.