Labelled as the sexiest job of the 21st century by Harvard Business Review back in 2012, data science continues to be an in-demand career choice for many even today. As a leading Data & Analytics firm, our ability to enable our clients to gain disruptive advantage using the power of data is largely attributed to our home-grown talent. This is true for data science as well.
We asked Sanket Ninawe, our data science CoE lead, a few questions to understand his approach to identifying, mentoring and nurturing data scientists at Infocepts. Read on to know more.
What’s your selection criteria for fresh graduates to be part of your data science team?
Many students want to become data scientists fresh out of college and when they come to us, our job is to assess how well they understand data structures, algorithms and programming. As part of the interview process, we review the courses and trainings undertaken by them. We consider students who have undertaken courses like computer science algorithms, statistics, or machine learning in college. We then proceed to test their knowledge on computer science concepts, algorithms, databases and Python.
What happens after selection? Do you conduct any training or orientation for them?
Yes, we put them through an intensive two-month boot camp where our instructors introduce them to various data science concepts and algorithms necessary for building machine learning models. It is an exhaustive training program designed to prepare them for handling a capstone project. As a part of this project, they are required to work on a use case and present their solution to the data science CoE.
Are your trainees project-ready at the end of your bootcamp?
No, they must complete some more add-on trainings such as natural language programming, deep learning, and more concepts. While we train them on these concepts, we also help them understand cloud technology because data science models are typically hosted on the cloud. AWS, Azure, or Google Cloud being some of the popular choices. So, we make them familiar with these platforms over the next few months.
Once we are confident about the progress they have made, we make them a part of our data science CoE. We further evaluate their progress when they become a part of the CoE and provide any additional support they may require. They may then be aligned to a POC or use case we may be working on. It gives the new data science members a chance to gain hands on experience to use their newly gained skills.
What next after their trainings and hands-on PoC engagements?
They begin working as junior data scientists on our client projects, where they will be guided by a senior data scientist to build data models with live data. We closely monitor their performance on the project and when they reach sufficient threshold, we recognize them as qualified data scientists.
After a period of about 3 to 4 years, they become eligible to be regarded as senior data scientists. While on their way up there, they are also expected to help us grow our team. We involve them while conducting interviews including technical rounds. This gives our data scientists an opportunity to explore how data scientists from other organizations work.
How do you ensure that your data science team stays sharp?
We encourage our data scientists to build point of views on latest technologies or new concepts that might be trending. They can spend some time in between projects to learn any new technologies that we may require in the future. The team has the freedom to choose the area of expertise they are most interested in like machine learning, NLP, image recognition, and so on.
In addition, we ensure that our data scientists are confident in communicating externally because our clients are often not well versed with data science concepts. Our team should be able to clearly explain how they have or will build the required data science model. To that effect, we assist them in creating presentations and we also have a monthly forum where they can showcase their work. The forum enables the team to learn from each other while evaluating the rationale for the choices they make to build their data science models. Everything put together helps them to excel in their roles.
Any closing thoughts you would like to share?
Infocepts was recently recognized by Analytics India Magazine as one of the top data science companies to work for in 2022. This was a proud moment for all of us. The average age of my team is 25 years, mainly comprising of fresh graduates and some senior, experienced professionals we have hired. Also, I can proudly say that our retention rate is around 99%.
We are looking to hire both fresh and experienced talent to grow our data science team. You can visit our Careers page to apply.
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