INSAID is proud to mentor students who show exceptional qualities to become World-Class Data Leaders. We’re a part of their journey, overcoming hurdles and celebrating their achievements to ensure they achieve their highest career heights.
Today, we share the successful journey of Rohith, enrolled in our flagship program of Global Certificate of Data Science & AI with our readers to help them navigate through their career trajectories and get motivated by Rohith’s story.
INSAID: Before we start, can you walk us through your career journey and tell us more about your new role in detail? And how is it a jump from your previous role?
Rohith: In terms of career, I started with being a developer, I have been working in & around with data for the last 12 years, mostly involving the traditional data warehousing projects, which involves both ETL as well as development.
I have worked for five different firms such as Infosys, Oracle, Motorola, Tech systems, and then PwC. Almost all of my career has been in and around data. In terms of the current roles and responsibilities, we are working for an energy utility sector plant where we are doing a Fusion HCM implementation. There is an Oracle product called human capital management, for that product we are building a backend Data Analytics and enterprise data analytics platform.
INSAID: How is it different from your previous experience? What have you done earlier? You had mentioned that you have worked with Data previously as well, so how is it different from being a Data Analytics Manager or being in that similar role? Are there any Data Science or Machine Learning applications that you have applied on a day-to-day basis?
Rohith: As of now, the scope for Machine Learning is very limited within this project but the reason I’m wrapping up is that since we are a consulting firm, we work across platforms that involve data. Almost all the work verticals, industry verticals needs Data Analytics implementations. In my current project, the scope of machine learning is very less because of the requirements and the method of the reporting is traditional, that’s why the scope is pretty less. But here, we are mostly leveraging some of the existing ETL and BI tools for doing the day-to-day like ETL, as well as reporting activities in terms of roles and responsibilities in the project. So basically, the overall responsibility of the delivery of the project lies on me from an official’s perspective, where I take care of a team of close to 10 people that involves both ETL as well as reporting developers.
Basically, we are working across multiple modules of HCM. When I say Human Capital Management, it is similar to your workday or other tools, which are there in the market that helps in taking care of payroll time, labor hiring, talent management, performance management, anything a typical organization needs to run their HR functions- whatever they need is what this tool provides but the tool stack is from Oracle. So, PwC is doing the application implementation and we are also building the enterprise data analytics platform as well.
INSAID: Okay, that’s really good. So at INSAID, which program and batch were you a part of? And what was your vision while joining and venturing into data science and machine learning? What was your inspiration behind it?
Rohith: I enrolled in the GCDAI end-to-end program. I am part of the August 2020 batch. Machine Learning and AI are typically a part of every organization which is trying to leverage some of the latest applicable data science programs. We slowly incorporate it in most of the consulting work we do. Even though machine learning is not straightaway implemented, an organization is looking from a perspective of say 80-20 where 80% is still relying upon the traditional way of getting the work done, that could be ETL, reporting or whatever traditional reporting tools that the organization is trying to leverage. Some of the clients are slowly getting into building data science, machine learning, and especially AI platforms. We have seen them become more evident where the clients are already on a cloud platform for their day to day reporting and ETL activities and on top of it they are building their AI and ML capabilities, which will help them to leverage some of the data which we already have and some that they have already brought in and then see what better use they can make of it.
INSAID: So are you saying that Data Analytics and the ETL management part is quite new in PwC?
Rohith: No, I mean we have been working on a Data platform for quite long but it is more like detail and reporting. But the way organizations are shifting gears, right, so basically, earlier, it was all on-premise environment. And now they are moving more towards the cloud environment. So when I say cloud, it’s all ages. If it is an Oracle stack company, they are moving to Oracle analytics cloud. And then if it is a Microsoft BI platform, they move into Azure. And then if it’s something new, they are getting into their AWS is getting leveraged. So this is a shift-shifting paradigm when it comes to the way data is getting handled. But once this shift is done, and when the systems get stabilized, that is where the clients are looking to leverage the AI-ML capabilities of the cloud stack which they are into.
INSAID: Okay, got it. Okay. So the next question is, what all tools and packages Data Science and Machine Learning have you mastered at INSAID?
Rohith: I wouldn’t say like I’ve mastered anything. I mean, the program so far has been really helpful. Because the problem is like, I have not spent enough time to read through all the concepts again or do the projects that have been assigned or work on any of these, like, the Python Grandmaster that happened, or it can be the project submission. So the back part, I couldn’t take her because of your project commitments, which I have, along with her like managing two kids at home. So yeah, I mean, I don’t say like I’ve mastered anything, but really like, the last few sessions, especially with Deepesh is like, it’s been really good. That is one feedback, which I wanted to give, the overall Machine Learning program is going well.
INSAID: Okay, that’s good. That’s good to hear. And so what were some of the initial challenges when you started off with this course at INSAID?
Rohith: Yeah, so the initial challenge is basically like, stats and math were forgotten for sure. Because I did not leverage it for quite some time, in any of our careers for the last 10 to 12 years. So some of the other things were like EDA or anything related to SQL underneath in & around data, those are the areas which I am pretty much confident in and we are dealing on the same basis as we work on it, the only thing is the way things are done is a little different when it comes to leveraging the Python libraries or any Machine Learning Algorithm. But in terms of SQL, we were doing more or less the same.
I don’t think we were leveraging statistics or mean median mode, to an extent what we do in machine learning, but outside of it, the EDA was more or less like what we have been doing in our day-to-day life. So the stats part was initially a little difficult, especially the machine learning algorithms was a little tricky but with Deepesh’s refreshers, all those doubts are getting cleared and it’s much better than what it was.
INSAID: Okay, so I guess your favorite faculty at INSAID is Deepesh?
Rohith: Yeah. So far I feel like Deepesh is really good.
INSAID: Okay. So, what do you think inspires you about him like what is that particular thing which you like about him or his teaching techniques?
Rohith: So, I have been a faculty as well like teaching traditional ETL tools and reporting and all of this, I mean the way like, so, what I feel is one is like, it has to be interactive. And then the one thing that he does very well is basically to make sure that everybody comes to a level of understanding at some point of time and then gets on with the higher level of concepts.
So, that is very important for any faculty to do to make sure that to bring the entire class to a certain level of understanding before the higher level of concepts are attempted. So, that would help like each and every individual’s like, it can be on any scale, right. So, each individual’s learning capacity or the learning hours, which he puts in over a week can be a little different based on, how he is placed, like in which part of the career he is, and then how his family likes life and balance and all of that. So, the number of hours everybody cannot put in the same hours like efficiency can do or bachelor can do.
So considering that the patient methodology where it helps us like across the board, irrespective of what walk of life you are, and then how many hours you put in, he helps you to get to a certain level of understanding and from where he takes it forward & that would really be very important for anybody to do it.
INSAID: Okay, that’s excellent. So can you describe the hiring or interview process for your position? Like the new position you attend? Or are there any interview rounds?
Rohith: No, I moved up the hierarchy within the same organization. And I had other offers also from other firms, but I decided to stay back with PwC and take the higher-up role.
INSAID: So basically it’s a promotion, I see?
Rohith: Promotion, yeah.
INSAID: So, the last question will be, what would you like to advise the mid-career professionals who are interested in data science and want to take up data science roles in the future?
Rohith: Yeah, so in terms of the number of positions or racks that are opening up, especially for data science students, right, and the demand, especially in the last three months, the search for demand is huge. Even now, like within our firm, we have close to 200 positions open, just for Data and Analytics professionals. It can be data strength, it can be like ETL reporting, or it can be anything to do with the data. So there are various positions that are open. And in terms of like, the clients, leveraging some of the latest tech stack coming to like Data Science and AI and ML, there is a lot of shift of the way it was built earlier. And the way it is getting built right now.
Even like a one and a half, two years back, the scenario isn’t the same. Because of the leverage when it comes to leveraging like AI and ML, it was not that much. Now the cloud capabilities coming in has helped to ramp up the pace, the way like whole data science was built. Right. So that’s where like any professionals, maybe like, I mean, this is even advisable for the campus hirings, which we’ll do, right? So whoever, like has this kind of capabilities or whoever has explored, especially the cloud, aim, these three would surely play a major role, either in terms of career growth, or in terms of getting into like, very good positioning within the industry.
INSAID: Okay, Do you like to add anything else? Like regarding the evolution of Data Science or anything?
Rohith: I think yeah, this is pretty much like what I wanted.
INSAID: Okay. Thank you so much. Really, thank you so much for your time. We wish you all the best for your new role. We hope you succeed beyond measure in your new responsibilities!