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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.

During my tenure at Cisco, I participated in a project where I collaborated with a devops team and testing team to accomplish the resource allocation . At the end of the project, each intern received an evaluation from our direct supervisor. I was surprised to find that my performance rating was lower than I expected, given the contributions I felt I had made.

Task:I wanted to understand the basis for the rating and felt it was important to clarify any discrepancies between my self-assessment and the supervisor’s perception. My goal was to maintain a positive relationship while advocating for the work I had done.

Action:I requested a brief, one-on-one meeting with my supervisor. In that meeting, I calmly listened to their feedback to understand their perspective fully. Then, I shared specific examples of my contributions:

• How I had taken the lead on documenting the current resource allocation and new resource allocation based on my experience.

• My involvement in proper resource allocation resulted in 25% reduction of P1 issues due to resource allocation.

• The additional responsibilities I took on to get all resource approvals and helm script changes went unnoticed.

I also provided the documentation and metrics I’d been tracking to illustrate the impact of my work. By centering the conversation on factual evidence and outcomes, I was able to highlight aspects of my contribution that might have been overlooked initially.

Result:After reviewing these points, my supervisor acknowledged that some of my behind-the-scenes work wasn’t fully visible to them.  we agreed to have more frequent check-ins going forward. This experience taught me the value of proactive communication and documentation, ensuring that both my manager and I have a clear view of my responsibilities and achievements.

Comparing current KPIs values with the previous ones from past months and the initial definition

As a QA leader I had to choose between run a full check of a new functionality that wasnt very popular or use or let the change pass over with the aprove of the PM for some time saving. I choose run the full check, take a few days more, but when the functionality goes to production it works well, without any bugs.

i think about my audience and what i want them to learn from the conversation so we can arrive at the right co-constructed decision. tha tmeans i communicate in the tone thats strongest for me, first, then adapted in voice for my particular colleagues. im consistent, but then meet them where they are or in the way theyre most likely to engage.

It helps to keep updated with latest technology development whereby enabling faster/ efficient/ better feature / experience delivered to customer. It might even open new opportunity for the company if senior leadership is able to think ahead.

In addition to external learning, it makes sense to keep internal knowledge sharing to avoid multiple work on same feature or leverage existing stacks for faster delivery or learn from others failure.

There are roles for which there is no direct course / education formally done but people need to learn primarily in leadership roles.