The data science domain—the “sexiest job of the 21st century” is on a rapid rise and companies across the world are hiring data scientists, experts. With time the popularity of data science is just getting bigger. According to one of our studies, demand for highly-skilled data science professionals has reached such a level that there are currently over 97,000 job openings for analytics and data science in India.

To spread the word more about the data science domain, we have produced articles ranging from “ habits to adapt to become a successful data scientist” to “brutal yet honest truths about the data science job roles”.  However, in this article, we won’t be talking about how tough it is to become a data scientist or what are the perks a data scientist gets.

This time it is about what makes data scientists feel appreciated and to get a clear picture of this, we reached out to 5 data scientists from the industry who have shared their experience.  

The Impact Of The Model

From creating a hypothesis to actually building and deploying a model, a data scientist spends a significant amount of time. S/he even ends up working extra hours just to make sure that the model works perfectly fine and solves the problem. Also, the process doesn’t stop just after deploying the model — a data scientist must keep track of the performance over time and make adjustments as needed. So, seeing a model delivering value is definitely something that makes a data scientist feel appreciated.

“For me, my model actually creating an impact gives me a kick. How many processes we could automate. How much money was I able to save for the company is the best feedback.” — Himanshu Negi, Sr. Data Scientist at ecolabs.

“Impact of the model on the business excites me; it can be in the form of revenue or Customer satisfaction.” — Sunil Rathee, Sr. Data Scientist at Swiggy.

When EDA Itself Solves A Problem

There are many businesses across the world that doesn’t want to leave their comfort zone or traditional way and go with a data science model. And as a result,  most of the data science models doesn’t see the light of day. So, when a data scientist provides insight out of cluttered data that has the potential to solve a business problem, it is something that drives appreciations.

“While doing EDA(Exploratory Data Analysis), if we can give provide some insights which can solve a problem or can give a business a new angle of fresh thoughts, it generates confidence in the Analyst/Data Scientist. And also, it increases the probability of utilizing the model created by the Analyst/Data Scientist.” — Wali Mohammad Khan, Sr. Data Scientist at Cognizant.

Improving Model Sustainability And Accuracy

It is no surprise that to get a model match the needs of a business and solves the most complex business problems it has to go through a lot of iterations.

“Building a model is hard and deploying it on real business and deliver results is even harder because while building ML models for business, data scientists to keep in mind that the business will change, and priorities will shift. And many models fail because either they fail to sustain, or they fail to deliver accuracy. So, when a data scientist spends his/her valuable time to get a model back on the track, making the model work flawlessly or at least make it accurate to a great extent, it is definitely a feel-good moment for a data scientist,” Avaneesh Kumar, Senior Data Scientist at ExperienceFlow

When You Derive Lot Of Latent Insights Which Has A Lot Of Value

Companies across the world spend huge amounts of money on data analytics — they seek a lot of insights from the data. However, there are instances when a data scientist might miss out on some hidden yet important data. So in order to extract as much insight as possible, data scientist perform extensive EDA and extract interesting information hidden in the data. And it definitely feels good when a data scientist successfully derives those latent insights.

“Most of the inferred latent insights will have valuable information and it can be used for taking business decisions,” said Sudharsan Ravichandiran, Data Scientist at Param.ai and author of Hands-On Reinforcement Learning with Python.

Knowledge Sharing

The data science industry is vast and the learning process never stops no matter how senior or experience you become. So, when a fellow data scientist seeks help from you and you sort his problems out, it is definitely a moment that gives you happy vibes. Also, many organisations have a training program for newly onboarded data scientist and the senior data scientist are the mentors of the program. Teaching and knowledge sharing is a noble job and when you teach something that has become so important to the industry, the feeling is definitely different.