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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.

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 think about my audience and what i want them to learn from the conversation so we can arrive at the right co-constructed decision. tha tmeans i communicate in the tone thats strongest for me, first, then adapted in voice for my particular colleagues. im consistent, but then meet them where they are or in the way theyre most likely to engage.

I try to understand their point of view and be open to new ideas. If I listen without being defensive I know  the other party will also listen. That way we can take the best idea from both and come up with great solution.

1. Situation: Our cloud bill had been steadily increasing, but engineering teams lacked visibility into which workloads were driving the cost. Leadership wanted accountability, but SREs like me knew that finger-pointing wouldn’t solve anything without data.

2. Task:I wanted to design a way to give each team ownership of their spend — without adding operational overhead — while also uncovering unused or over-provisioned infrastructure.

3. Action:I built an internal “cost dashboard” that pulled billing data from our cloud provider’s API, tagged resources by team and environment, and visualized it by service, team, and project. But the innovative part was integrating it with our CI/CD workflows: before a PR deploying new infra could be merged, it would estimate the monthly cost impact. I also built a Slack bot that sent weekly team-level cost summaries and flagged idle resources.

4. Result: Within two months, teams identified and shut down $8,000/month in unused dev resources. More importantly, it shifted the culture — infra cost became part of architectural discussions. Finance even started using our dashboard during quarterly planning instead of relying solely on billing exports.

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.

while reviewing one of the older projects that was currently implemented in the company I noticed that there was no alerting in place if the data pipeline breaks, the data pipeline has never been broken and it never failed for the last 2 years. it was a pipeline where the data would be dropped into an sftp bucket by the customer every hour and then it was moved to an s3 bucket where s3 triggers were implemented to moved the data in to queue. So in case there is an issue with the data not being processed we would only know when the we see a drop in volume. I identified it as a potential issue with the system and worked on a proposal to have monitoring in place so we are notified when there is an error. I worked with the program manager and devised a plan on how to implement the alerting and the time lines as well as the effort that will be needed to implement it. I mapped the impact of not having it to the current goals of the company and then put forward a proposal of implementing the alerts to the business and took their buyins. after getting the buyins I proposed the task to my team and some of the sr devlopers were not too keen on implenting the alerts, I gave the task to some of the Jr devlopers and the alerts were implemented. after the implementation we discovered that for some hrs in a day we werent getting any files so instead of getting 24 files in a day we were getting 20 files a day in an average. This was rectified by talking to the customer and the missing data was solved. but this made the whole team realize the importance of monitoring and alerting. It also bosted the moral of the Jr developers seeing the direct impact they made to the business goals

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.