The most comprehensive bank Interview Answer Review tooling available online.

I look after our email platform and our existing solution wasn't flexible enough to handle targeted messages at scale. We were missing an opportunity with users to send personalised messages at the right time to help increase engagement. I embarked on a project to implement a new marketing automation software. This project required alignment across our product, data, retail, security and legal teams. I spent time researching the availble tools in the marketing and workshopping requirements with relevant stakeholders, we created an RFP and sent to a number of suppliers. Each round of the RFP I assigned a score to the company and gathered feedback from stakeholders. This ensured the decision making process was fair and we had strong evidence when sharing the business case to senior leadership.

Once we decided on the supplier, I was responsible for implementing the tool which required support across various teams within the business. Implementing something cross-functionally

i was head of events in a student activity called aces and it was a magnificent experience as i gathered a team wich i chose by myself and my partner as they all were hardworkers and creative thinkers of course we had some trouble with them as some of them were being lazy about doing there tasks or procranstinating but we were able to motivate them each time so they would give their best

I see myself working at the intersection of engineering and quantitative finance ideally at a hedge fund such as Two Sigma. My past experiences at Harvard Medical School and internships at MITRE/rapStudy have given me an interest in creating scalable systems that are high performant and capable of ingesting large amounts of data for model development and research.  I strongly believe Two Sigma is the perfect place for me to work on these highly technical projects. Additionally, I also look forward to the prospect of entering leadership roles during my career, where I can play a more impactful role in prioritizing cultivation of engineering talent and projects in fields such as low-latency and data engineering.

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.

I’ve reached a stage where I feel I’ve learned a lot in my current role, but the growth opportunities ahead are limited. I’m looking for a new challenge where I can take on more responsibility, work on impactful problems, and continue to grow.

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.

speak to them one on one first. Kind of get friendly. If you are close enough, you can just directly say it to them that it is time theft. but if you're not close to them, ask them why they come in late first to see if they have a valid reason or if they already pre-arranged it with the manager. If yes, then stop and apologize and just move on. if no, explain to them whatever

At hyperface we got requirement from product team to build a nudge engine, this will be a configuration driven engine and when a condition is met based on the configured communication pattern it is going to send the communication. Example : If user have done 5 transaction is flikart and an offer is going on to do 10 transactions to get a reward we will nudge user based on the communication channel and rules that nudge him on whatsapp this much time and send him the push notification.

When this product requirement came , product wanted to build each and everything as a single engine, but at that time I realized that having a separate notification service which will take care of all notification related stuff and we will be able to efficiently scale that system and prevent having a tight coupling  will be very helpful , but when I proposed this product did not agreed as they thought right now there is no usecase for making nudge engine and notification engine a separate component.

At that time I dicsucces this with my fellow EM and came to know that they are scenarios where they need to send notification for example otps , push notification and everyone is handling this in a custom way in there own code based . With all this data I went back to product team ann pitched them that having this notification engine all this custom handling will be gone we will have better mechanism to control notification  track metricies and and improve the experience by handling failure scenarios .

with this data I was able to convenience the product team to build to different systems.

Question: Tell me about a time when you assisted a colleague in his work. What was the result?

Answer:

Sure. I really enjoy helping others on the team, especially when they’re stuck and not sure how to move forward.

S – Situation:
Once, someone on a neighboring team was struggling with a production issue for a few days. I noticed that he seemed frustrated and wanted help, but didn’t quite know how to ask.

T – Task:
After finishing my work early one day, I proactively messaged him on Teams and offered support. He was happy, but mentioned he wasn’t even sure how to describe the problem clearly.

A – Action:
So I sat down with him and tried to understand the issue. He told me that after one of his code changes went live, a dropdown component in our shared UI broke — its layout was completely messed up, even though his change didn’t touch that component directly.
I helped him debug the issue step by step. After ruling out rendering or layout bugs, I noticed the problem was actually due to a CSS variable conflict — the styles weren’t properly isolated, which caused unexpected overrides.
Together, we fixed the CSS scoping issue and verified the dropdown worked as expected.

R – Result:
The bug was resolved and the fix was shared with the wider team via email, to prevent similar issues in the future. My teammate was really grateful, and after that, our collaboration improved significantly.