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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.

Situation: During the first calibration of Non-Maturity Deposit (NMD) models, I was working with a senior colleague and a consultancy firm to implement these models. At that time, market rates were rising, and the consultancy firm suggested calibrating a key parameter, the "beta," to be highly sensitive to market rates. My manager agreed, but I disagreed, as I believed this high sensitivity could lead to inaccurate estimations in future calibrations.

Action: I engaged in discussions with my manager and the consultancy firm to fully understand their reasoning. They argued that a more sensitive beta could improve management of metrics like Delta EVE and Delta NII, which was valid. However, I pointed out that we lacked sufficient evidence to support such a high beta and that it might cause issues in future estimations. To address the disagreement, I conducted a sensitivity analysis, testing the model under various market rate scenarios.

Result: The sensitivity analysis helped us reach a compromise. We agreed to lower the beta, ensuring more stability in the model's performance. This adjustment allowed for a smoother transition of the parameter, ultimately improving the model's reliability while maintaining good risk management practices.