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I was managing organization wide restructuring program where goal was to map all resources with their delivery accounts to keep a track of account’s PnL and manage capability as cost centre. We also intended to design KRA and KPIs for each individual level to make our Performance Assessment data driven and transparent. This was a big change at employee level at professional and emotional level too. It meant changes in their reporting structures, extra reporting and performance management by all managers, clear KPIs alignment and selection.

Planning Level: We identified all the major business account heads, capability and other function head and organized a communication platform to make them aware and comfortable with the changes. We undertook their inputs on pros and cons and some caution to be exercised

Execution Level: We started executing these changes with L0 level employees and moved up to leadership team. Along the way we encountered some risks and outlier conditions to be managed.

Communication Strategy: We worked with all business and functional heads on our communication strategy to reach out to all the employees. Head multiple events and workshops to discuss the changes.

Data Platform changes: Evaluated all the data platform changes required and planned a phased out approach to bring those changes in the system as well.

However, when we were about to reach closure of this program, I realized that I have made a big mistake of readiness of our existing platforms to support all the changes required and existing data integrity. It really took a toll on everyone involved to clear up data accuracy before the changes could be introduced.

In my current role, I led a team of 6engineers, 3quality analysts and 3 partner account managers to build and deliver a product for enabling integration with tax authorities government portal and partner tech systems to systematically onboard the partners and consume, validate and pay their invoices in line with local authorities regulatory requirements. My role involved partnering with internal xyz transportation business teams of outbound, import and returns business, financial operations, procurement and controllership teams to align on the business and tech requirements, understand the tech integration dependencies, accounting requirments and define the MVP, change management and then drive the go to market strategy through a dial up. 

Question: Tell me about a time when you assisted a colleague in his work. What was the result?

Answer:

Sure. I really enjoy helping others on the team, especially when they’re stuck and not sure how to move forward.

S – Situation:
Once, someone on a neighboring team was struggling with a production issue for a few days. I noticed that he seemed frustrated and wanted help, but didn’t quite know how to ask.

T – Task:
After finishing my work early one day, I proactively messaged him on Teams and offered support. He was happy, but mentioned he wasn’t even sure how to describe the problem clearly.

A – Action:
So I sat down with him and tried to understand the issue. He told me that after one of his code changes went live, a dropdown component in our shared UI broke — its layout was completely messed up, even though his change didn’t touch that component directly.
I helped him debug the issue step by step. After ruling out rendering or layout bugs, I noticed the problem was actually due to a CSS variable conflict — the styles weren’t properly isolated, which caused unexpected overrides.
Together, we fixed the CSS scoping issue and verified the dropdown worked as expected.

R – Result:
The bug was resolved and the fix was shared with the wider team via email, to prevent similar issues in the future. My teammate was really grateful, and after that, our collaboration improved significantly.

I have been working on same project since last 3 years, I want to explore different projects and learn new skills and Technologies with current market trends

It can cause overfitting problem. Model with a lot of layers will train on different set of layers to enough information, which can be sensitive to noise. Then it can cause computational time to increase and operational costs.