The most comprehensive bank Interview Answer Review tooling available online.

As a qualitative researhcer I use various tools depending on the different stages of my research and project. For instance, in the discovery phase, where it involves more research and conducting interview or observation if I'm working on a series of remote interviews I use zoom or teams to record the interview session, and after finishing the interviews I use tools such as Dovetail , Nvivo, Atlas.ti, and Maxqda to transcribe and analyze the data and find the emerging themes. Meanwhile for extracting the teams techniques such as affinity diagramming and empathy maps are super helpful, and can help with generating the personas or journey maps that are useful tools for communicating the research findings and next steps.

I graduated in May 2024 and recently wrapped up my role at Bright Mind Enrichment. It was a great experience, and I stayed through my agreed-upon commitment.  At this point, I’m looking for a full-time opportunity that offers deeper ownership, long-term growth, and technical mentorship—especially in a full-stack environment like Atlassian’s.

I am pretty passionate about Reddit. In a world where media is biased and polarized, Reddit threads gives me a sense of how local communities view the world. It helps me get grounded in reality of what different cadres of people are thinking by browsing different communities. Reddit is very engaging, as in, you can comment and instantly receive feedback. While there is a lot of "internet hate" and "misinformation" in Reddit communities and threads as well, looking at a wider spectrum of communities within Reddit, especially the differing ones, helps you form a less polarized and biased opinion. I prefer contributing by asking open-ended questions and if I have an opinion, posting it respectfully as a question. I also respond to scientific minded questions that I have qualifications to answer. By selectively responding and answering questions with some due diligence, I keep the communities I am a part of thriving and truth-based.

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.

One project i'm proud of is my internship project which was a full stack project where i had the chance to create a prototype of a template manager , storing the data into a cassandra database, alongside using springboot and java to work on the module structure and life of the project, I then used js and react for UI. This prototype was then deployed and is now being used by internal payment team at jpmc, instead of going to search every resource, i store all the templates in one template manager and they can know just search the nav bar and get the exact template they need. I'm proud of this project not only because it was my first real project using the new languages and frameworks i was learning during internship but it also gave me the confidence of knowing and seeing that I can do this even though i dont have a degree in this field!

Situation:
While working on the CloudGate (CASB) program at Oracle, I had to collaborate closely with a lead engineer who was highly technical but often resistant to input from non-engineering stakeholders, including program managers like myself.

Task:
Our shared goal was to integrate CloudGate’s policy engine with a third-party SaaS provider under tight deadlines for a compliance milestone. Coordination was critical, but communication friction risked delaying delivery.

Action:
I focused on building mutual respect by first acknowledging his technical expertise and then shifting our interactions to be data- and goal-driven. Instead of pushing process, I came to discussions with clear risk/impact scenarios and timelines tied directly to customer and audit deadlines. I also adapted my style—fewer meetings, more async updates with technical clarity—which matched his preference. Gradually, we established a working rhythm built on outcomes, not personalities.

Result:
We delivered the integration on time, passed the compliance audit, and by the end of the project, had built enough trust that he started proactively surfacing risks to me before I had to ask. The experience reinforced for me that adapting communication style and focusing on shared goals is often more effective than trying to change personalities.

I once led a restructuring initiative aimed at aligning resources with delivery accounts and redesigning KPIs. While the planning and communication strategy were solid, I overlooked whether our data platforms could support the new changes. Just before rollout, I discovered serious data integrity issues. This setback required a major clean-up effort across teams, but we corrected course by prioritizing platform readiness and enhancing our validation processes for future initiatives.

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.