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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.

Earning the trust of a team is essential for effective collaboration and success. I approach this by:

1. Being Transparent: I prioritize open communication and honesty. For instance, during a project update meeting, I openly shared the challenges we were facing, which helped build trust among team members as they felt informed and included in the process.

2. Listening Actively: I make a conscious effort to listen to my team members’ ideas and concerns. When a colleague proposed a new strategy, I encouraged discussion and incorporated their feedback into our plans, demonstrating that I value their input.

3. Following Through on Commitments: I ensure that I meet deadlines and deliver on my promises. For example, when I noticed our team was at risk of missing a deadline on a key project, I took the initiative to reorganize our workflow, delegate tasks more effectively, and set up short check-in meetings. This allowed us to complete the project on time, reinforcing my reliability to the team.

4. Showing Empathy: I take the time to understand the individual needs and motivations of my team members. By being supportive during high-pressure times and recognizing their personal challenges, I create a safe space for open communication.

5. Encouraging Collaboration: I foster a team-oriented culture by celebrating collective achievements. After we completed a project successfully, I organized a small team celebration to recognize everyone’s contributions and strengthen our bond.

6. Being Approachable: I maintain an open-door policy, inviting team members to share their thoughts or concerns. This accessibility helps establish a friendly atmosphere where everyone feels comfortable engaging with me.

By implementing these strategies and sharing specific examples, I build a foundation of trust that strengthens team dynamics and enhances overall performance."

I led a project to implement a new feature in our loan processing system aimed at reducing API latency for faster loan approvals. Midway, I encountered data inconsistencies and latency issues caused by schema mismatches between the new API and our backend services. To resolve this, I optimized the data schema mapping, streamlined query performance, and introduced Redis caching for frequently accessed data, which significantly reduced the API call frequency and improved response times.

I also collaborated with QA to parallelize testing, allowing us to catch issues early. By continuously optimizing our microservices with adjustments to load balancing and asynchronous data processing, we increased processing speeds by 20%. While the deadline was slightly extended, our enhancements directly improved loan approval times and were a solid technical win for both the team and the user experience.

I’m my current role, during a core-team meeting I noticed conflicts between 2 team members, the MM and the EE. They were debating about why to include one component in the official BOM and discarding another one.

To understand their perspectives, I spoke with them separately in a friendly environment. The MM was frustrated because the EE didn’t provide any data or argument to discard the component that the MM suggested. This component had the same performance, was pin to pin compatible, and cheaper. On the other hand the EE explained he had bad experiences in the past with the component suggested by the MM and he refused to validate and qualify it to do not waste his time.

So… After being clear about the situation, I brought them together for a discussion and I emphasize to focus on the issue, not in personal discrepancies. Do, during the meeting, I encouraged both to consider the Project requirements, customers need and I worked with them brainstorming session.

Then, they agreed to meet with other stakeholders to get their inputs about these 2 components, they commit on a validation plan including both components. The one suggested by the EE as POR and the one suggested by the MM as alternate. They also agree to allocate 60% of the mass production volume to the EE component and 40% to the component suggested by the MM.

From my side, to ensure progress, I scheduled follow-up meetings with them to monitor their collaboration and for sure, I offered my support. Finally, both components are currently part of the product official BOM and these 2 guys improved their working relationship and communication.

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.