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In my role leading the launch of that new customer onboarding system, the pressure was really on. We had that tight three-month window and a lean team to build something that would be the first impression for all our new users. One specific hurdle we faced was integrating our legacy customer database with this brand-new system. The data structures were quite different, and initially, the data migration process was proving to be much slower and more error-prone than anticipated. This threatened to push our launch date.

To tackle this head-on, I didn't just delegate. I dove into the technical details with the lead engineer. We mapped out the data fields meticulously, identified the key transformation rules needed, and actually prototyped a more efficient data migration script using Python. This allowed us to automate a significant portion of the process that was previously manual.

Furthermore, to ensure we were building the right thing quickly, we implemented very short feedback loops. We'd build a small piece of the onboarding flow, get immediate feedback from a small group of internal users, and iterate based on their input. For example, early on, users found a particular step in the registration process confusing. Based on their feedback, we completely redesigned that screen within a couple of days, leading to a much smoother experience.

By getting into the technical weeds to optimize the data migration and by relentlessly focusing on user feedback through rapid iterations, we not only overcame the risk of a delayed launch but also delivered an onboarding experience that was significantly more user-friendly than initially envisioned. It was incredibly rewarding to see new customers move through the system so smoothly right from day one. That success really underscored the power of combining technical problem-solving with a user-centric approach

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.

Success could be measured in multiple different factors. An engineer or a resource would be performing very well in the team, stupendous technically but their relationship with the team should also be desirable. They should be open to collaborate, have good relationship, trust with the team, help cross functional team for collaborating with multiple business teams. Should be able to understand the problem providing right solution at right time, mentoring others in the team and always posses the ability to communicate with at most clarity. 

All of this is a 360 degree feedback that one could be considered successful working in an organisation.

To support the goal of making Windows OS the more stable it was important to reduce crashes due to driver and hardware issues. There are two types of drivers kernel mode and user mode. A fault in the kernel mode driver freezes the OS but a fault in User mode driver doesn't. My task was to convince all the teams that owned kernel mode drivers to migrate them to user mode. We identified 90 different candidates and developed a plan to convince them to migrate. This involved providing a clear how to migrate documentation, automated tools and test cases for migration, we also updated the user mode driver framework so its interface exactly matched kernel mode driver framework. In some cases, my team had to do significant portion of the migration for the candidate team. After we migrated some drivers, we shared the best practices and results with other teams to increase adoption. Over the span of 1 year we were able to get 30% of the drivers migrated to UMDF which greatly increased the OS stability and reduced memory requirements of the OS by 40 MB. To summarize, I successfully persuaded many teams to migrate to user mode driver by providing clear documentation, rationale for migration,  test cases, automated tools and finally showcasing migration success stories in the organization through regular status updates.

I would talk to him 1:1 and mention that I have noticed that and would like to know if there is any reason. 

if the coworker has no reason I would explain the disadv of arriving late and Ala him to be on time 

In projects associated with infrastructure there is usually a common point of decline close to the end of the program.

When working on Flutter Infrastructure I found that managers, technical program managers and product managers get excited during the project planning and software engineers get excited during the design and implementation phases of the program. However there was a decline in the velocity and enthusiasm during the migration and the deprecation of old systems. This is because these phases require careful planning, a lot of communication and coordination. I have managed this situation with my teams with clear and open communication that a project is completed only after the data and production workflows have been fully migrated and the old systems are being phased out. Having this clearly defined as part of the program plan not only simplifies execution and communication but it also helps collecting the details required for the program retrospective and the program closure.

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.