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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.

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.

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 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.

In the first company I worked as tech lead, even though my performance rating was good, I received a 360 degree feedback on my interpersonal skills as well. One of them said that I sounded a bit rude in couple of discussions. When I asked for the instances, I got examples where I got the feedback from and not whom. So I had to explain my manager that it was not intentional and the aggressive demanding nature of mine could have been perceived as rude. But I considered the feedback and identified, corrected myself, toned down the way I speak in certain instances which made a set of teams in my org ex: production support teams reach out to me more often now for guidance. So this helped me gain valuable experience and unlock a totally different perspective in terms of collaboration with other squads in the company.

I was managing organization wide restructuring program where goal was to map all resources with their delivery accounts to keep a track of account’s PnL and manage capability as cost centre. We also intended to design KRA and KPIs for each individual level to make our Performance Assessment data driven and transparent. This was a big change at employee level at professional and emotional level too. It meant changes in their reporting structures, extra reporting and performance management by all managers, clear KPIs alignment and selection.

Planning Level: We identified all the major business account heads, capability and other function head and organized a communication platform to make them aware and comfortable with the changes. We undertook their inputs on pros and cons and some caution to be exercised

Execution Level: We started executing these changes with L0 level employees and moved up to leadership team. Along the way we encountered some risks and outlier conditions to be managed.

Communication Strategy: We worked with all business and functional heads on our communication strategy to reach out to all the employees. Head multiple events and workshops to discuss the changes.

Data Platform changes: Evaluated all the data platform changes required and planned a phased out approach to bring those changes in the system as well.

However, when we were about to reach closure of this program, I realized that I have made a big mistake of readiness of our existing platforms to support all the changes required and existing data integrity. It really took a toll on everyone involved to clear up data accuracy before the changes could be introduced.