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I stand out because I can anticipate the needs of the team and my leaders. I follow bottom up leadership and I am empathy driven.

During final stages of testing upgraded version of core banking applications, we encountered a defect which required resolution from development team. After dev deployed the defect, I had very limited time to thoroughly retest and perform regression.

My task was to perform retesting and make sure there are no major defects after the release.

After resolution, based on my prior knowledge of the apps I grouped those testcases that had a potential impact to run automation regression to speed up the testing. While I manually tested the defects with some edge case scenarios. 

I kept the cross functional team updated about the testing progress and the risk involved if testing wasn't completed on time. 

But, I was able to mitigate the risk of not completing the testing on time and major risk of unexpected issues with the help to prior knowledge and proactive communication.

Thus, I learnt from this incident to carefully strategizing the situation and collaborative approach can help to mitigate risks.

At my previous role, I was leading a fraud detection AI model deployment for a financial services client. The model aimed to reduce fraudulent transactions by 25% while maintaining a low false positive rate.

I was confident in the model’s performance in the test environment, but when we deployed it in production, we noticed an unexpected spike in false positives. Transactions flagged as fraudulent were legitimate, causing disruptions for customers and increasing manual review work. To resolve this, I:

1. Investigated the issue by comparing training and production data.

2. Identified data drift—the model was trained on historical data, but recent transaction patterns had changed.

3. Worked with data engineers to implement real-time data monitoring.

4. Retrained the model with up-to-date data and adjusted hyperparameters to improve generalization.

This fix reduced false positives by 25%, improved fraud detection accuracy, and ultimately saved $500M in operational savings across multiple initiatives. This experience taught me the importance of continuous model monitoring and proactively accounting for data drift. Now, I always incorporate data validation pipelines and feedback loops into AI deployments to prevent similar issues.

Situation/Task: I was working on a migration project which had lots of ambiguity and external dependencies. I was asked to meet a deadline which I could not commit to.

Action: I got time estimates from dependencies and built a burndown chart with developer time taken and added buffer for oncall/vacations. Got it reviewed by manager and director and estimated the number of devs it would take. We did not have enough devs so some features had to be moved below the line for the migration to be IA to teams, with other features as fast follow.

Result: I was able to meet the stripped down product deadline and not all teams were able to immediately use our product but on balance it was good.

While working on a navigation redesign project, my team and I found that a colleague on another team (a GTM person) was very unhappy about the redesign project. There was a lot of chatter that she was unhappy and had spoken with leadership about her misgivings, but exactly what was bothering her was unclear.

The designer on my team and I decided to set up a 1:1 meeting with this colleague in order to understand exactly what the issues were. We learned that her team was in the middle of rebranding their product and she was worried that the launch of the redesign would undermine the rebrand effort.

We assured our colleague that the launch of the redesigned navigation would not happen for a few months, still, long after their rebrand and the buzz around it would have settled. With that assurance, she was no longer anxious about the redesign project, and we were able to move on with the work.

Simply having a conversation, understanding what the issues were, and coming up with a resolution was all it took.

n one project, we were under pressure to release a feature quickly, and most of the team wanted to skip certain performance optimizations to meet the deadline. I made the decision to delay the release slightly to refactor critical parts of the Java backend and implement proper caching and database indexing. Initially, it was unpopular because it felt like slowing down progress, but I explained the potential long-term impact of ignoring performance issues. After implementing the changes, the system handled 3x the load without issues, and we avoided costly production incidents. This experience reinforced that as a senior developer, sometimes the right decision isn’t the easiest or most popular, but it’s about ensuring reliability and scalability for the product.

Success is when you accomplish a goal you had set either in your career or personal life. It can be measured in financial output or emotional contentment.