Today in our Accredian Success Story blog, we introduce you to Rahul Ghosh who is an Data Engineering leader at Delta Airlines and was a part of the GCDAI, 2019 batch. He shares his journey pursuing Data Science and Machine Learning and shares about all his achievements.
Q1. Tell us about your current role and background
Hi, this is Rahul. I am working for Delta Airlines as an Engineering Leader.
At Delta Airlines, me and my team are responsible for several upscaling and modernization of legacy platforms built on Codejava.net, Angular, etc. to newer technologies using Spring hibernate, react JS, node JS and a few other technologies.
Our team is responsible for modernisation from older SOA based applications to micro services and API’s. We are also responsible for rewriting some of the existing processes using cloud native technologies so that it is easy to move to cloud without affecting our upstream and downstream processes.
My current role is to manage all these applications, I also work as an Architect within the team, work as a Team Manager and as an Engineering Lead.
Q2. What challenges have you faced while exploring Data Science?
When I started exploring Data Science, it was some 5-6 years back. I was doing my own homework and trying to connect with several people who have started doing it.
It was a little bit newer also in the market, at the same time and it was not as prominent as it is in today’s world. Since I come from a bit of a legacy background, like from mainframe and codejava background, which is more of a legacy these days, I decided to upskill myself on Data Science.
I wanted to specially focus on AI ML, as I was not sure where to start and what to learn to ease my journey in Data Science. While doing so, when I was exploring several organizations and institutes who were offering similar programs or little bit of different programs.
It was difficult to judge who is good, where you can invest more of your time and money and get a better output that will provide you with applied knowledge.
This is because we have theoretical knowledge, but we need to have applied knowledge these days as well. So, that was the challenge which I was facing.
Q3. How has Data Science helped you in your new role?
The knowledge of Data Sciences has helped me tremendously in getting several opportunities from various organizations and cracking several of the interviews. It even helped with several Hackathon, Datathon events which happened at my previous companies, and which was also happening across several other companies like Take Geek, Machine hack and Kaggle as well.
At hackathons I secured quite a good rank and on the leadership board, I was once in the top 10. Machine hack was organising one of them and even there I was one of the top 10 people for some time, I was very happy to see myself there.
So, it helped me a lot in getting the interviews, in tracking datathon, hackathon events. So, using my knowledge I was able to automate, since I was focused more on the ML part of it and slightly on the Deep learning part.
I was able to automate some of the existing manual processes like auto recon, the auto reconciliation process, which happens in every bank banking world. I was able to automate that using ML and that helped to tremendously reduce the human errors, which happens when this process of auto recon is done manually by a group of people or group of end users, the operation users and it made them process more ‘Straight Through Processing’ that is STP.
There was less of a manual intervention and as you get the data you go through the ML process and it helps you do the auto recon process to a great accuracy.
Q4. What was the interview experience at your current company?
My interview was basically more on the design patterns, on the various agile methodologies that I have used throughout my experience on high level designs, the architecture, the tech stacks, which I have used and solving some of the problems using those tech paths, which will be helpful when we will be using a particular technology.
That was something which I was interviewed for on the technical part, more on the architecture side and on the design patterns. Since this role also has something to do with people management and project management, I was interviewed on the project-people team management aspect for the non technical part of it.
I was elaborately interviewed on micro services API’s, architecture patterns and cloud. The interview experience was very good, it was the usual 3-4 rounds, which happened. First being the technical round with architects and then with the manager, the second one with the recruitment manager, recruiting manager and his director, director boss and the third being the HR interviews.
Q5. Can you explain one of the projects under your leadership?
One of the projects I would like to elaborate on is the ‘Auto Reconciliation Process’, automation which was done on this process, and which I was quite instrumental in building from scratch in my previous organisations like Societe Generale and Wells Fargo.
Normally in every banking process, they do have reconciliation done between both the legs. So, when I say legs, it means both streams, like whenever you are buying something, you will have a buy side you will have a sell side.
Whenever you are purchasing something you will have the goods of securities which you have traded for and the money transactions. So, these are called the legs where you can say the buy and the sell side is one leg and the goods you purchased are transactions you traded for and the money transactions which you did to buy those things is the other leg.
The reconciliation process is basically to receive and see what you have paid for you, and that have received those many amounts of goods or securities for e.g. if you have paid 10 rupees for something, you should have bought a product which is valued at 10 rupees.
Now if I have done a lot of trading in the market, at the end of the day, I would like to see or two companies will like to see that if I paid X amount of money in the market, how much securities or bonds or cash or anything which you have got as a part of that trade is worth it.
You would definitely like to do the reconciliation. So this is common for every banking or any business which you have today. We wanted to use the old and huge amount of data for our reconciliation, which was already present.
That is the project I wanted to elaborate on, which we did, though there are many but since I wanted to stick to one ML project, this is something which I felt was a good example to give.
Q6. Why Accredian? How did it help you?
Accredian, I will say it was very instrumental in laying my Data Science foundation and Suchit sir was quite helpful. For all the beginner classes we used to have discussions with him. He used to take the sessions and it was very helpful.
The way he used to impart confidence, helped not only me but others in gaining expertise using Python language, which was completely new to me. The weekend session recordings after each lesson and the materials were quite helpful to give me a good hold on ML topics in specific and Data Science in general.
The sessions used to be quite hands-on, with lots of classroom discussions around the various ways by which a problem can be tackled. All the people, like all the teachers or coaches who were in other classrooms, used to appreciate people taking various ways to solve a particular thing, to tackle the problem and various angles.
They used to give us an open hand and allow us to explore how we can go about solving a problem and what are the various things which come in our mind when we come across a problem and that was quite helpful.
The regular exams and the projects helped me a lot in learning. Implementing AI ML project as a team which happened in the capstone project, which I did at Accredian helped me as well. I’m very grateful to Accredian for providing me with such a platform which helped me to upskill and excel not only in data science, but also in the corporate world.
Q7. What advice would you give to Data Science beginners?
You need to analyze and decide what interests you, what you would love to do in your career for several years to come, you can pick up from various things which are present in the market like Data Science, IoT, Blockchain and Cloud. Each of them is big in itself.
It should keep motivating you to work further and to contribute more towards the industry and also towards the society. That is something which you should decide. Once you are sure and you are very keen on taking up Data Science, you like data, then you need to select a good institution who can not only teach you but also guide and acquaint you with all the tools and techniques needed to handle problems related to Data Science.
They should be able to pick you up from the novice state where you don’t know anything and should be able to bring you up to a level where you should be able to start exploring based on the past experiences, knowledge and skill set which you have acquired all throughout your journey.
A good institution like INSAID is very instrumental, that is the main decision which you need to take. There is tremendous growth potential in AI ML, as we are still in the nascent stage of AI and in the coming years we’ll see rapid development towards self-driving cars, computer vision, augmented reality (AR) and this will impact humanity and the coming generation greatly.
The more we are acquainted with not only theoretical but applied Data Science knowledge, the more growth potential we will have both in our career and as entrepreneurs.
INSAID is quite good it can take you from a beginner to an expert level and then lets you take off. So I would like to thank INSAID for giving me this opportunity to speak and I look forward to learn more from INSAID and learning more about Data Science.
We hope you found this success story interesting. If you have any Data Science questions, please fill this form and we will get back to you.
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