The most comprehensive bank Interview Answer Review tooling available online.

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.

In designing a new website, first analyze the content and target audience data to determine objective and threshold values in terms of site access and minimal and peak access times. Once you've determined the values you can design an N-tier server architecture to reduce load on single section by having an SSL decryption server, a app server, a database server, a content server and then multiple load balancers set up to distribute access loads based on your objective and threshold values. In a cloud based architecture, the use of individual docker containers for app / DB / encryption, etc accomplishes the same goal, with a kubernetes disturbed framework latency can be reduced and speed increased. 

To earn trust within a team, I believe it’s important to start with a small, achievable task and deliver a high-quality result on time. Once I’ve proven that I can deliver reliably, I gradually take on more complex tasks, building credibility along the way.

Equally important is asking the right questions and actively participating as a team member. At XYZ, when I joined a new team, I gained trust by ensuring that we only committed to tasks we could realistically deliver. Once we met our goals, I took the initiative to showcase our accomplishments to leadership, highlighting the team’s impact and visibility.

By consistently delivering results and advocating for the team, I was able to earn the trust of both my teammates and leadership.

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.

When I started to as PM in China, I was responsible for managing a Project for an important customer. After few days in charge of this program, the Engineering Manager provided feedback that my Project schedule was not feasible based on the testing requirements defined by his team. This automatically delayed the first EVT delivery.

Then, immediately asked for a meeting with the Engineering team to get more specific feedback and understand what was missing in my schedule, what were the technical requirements and why these testing was so important.

I took the inputs and spent some time analyzing in detail all technical requirements, reviewing with different core team members all activities Lead Times, dependences between tasks and looking at other projects data to identify patterns.

With all these inputs, I created a new plan. I coordinated a daily opening and closing meetings with the core team to monitor activities and prevent task duplication between cross-functional teams. I also highlighted the issue to my manager and explained him the root cause of the problem, and the new plan. Together we informed our customer about the delay and kept working together to reach the new committed schedule.

Finally, we delivered the EVT units to our customer according to the new schedule. My manager also acknowledged the effort to enhance the customer satisfaction, and the experience showed me the importance of being honest and proactive in communication. From this day I also keep 2 weeks of buffer in my programs schedule

• There was a time the team was developed a search optimization capbility for users based on given requirements by product manager, so it could add more search filters for faster accessibility so . I asked the team to create demo on dev environment and to show customer, in order to make sure it is aligned with users need. I scheduled the meeting and we showed the demo, suddenly we undrestood all the usecase were not covered by program manager which lead to poor design it means some of the file type were not identified by specific locations. And that wasn't a feature for customer. On the other side we didn't have enough time. Since we needed to release this feature on time and as manula accessibility got block the program ​
• ​
• So I pushed the product manager to schedule meeting with diverse useres to make sure to capture diverse uses cases. I put together team and we discussed on options and tried reestimate we identified that by one more api resource we can have this feature on time. So I escalae that to leadership since the timeline was crucial for them, they assign api resource from india Lesson learned: 25 percent ​

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.