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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.

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.

Parametric means that the underlying distribution of the data is typically normal, and assumes a specific data distribution. Parametric assumptions are required for tests like the two sample t-test, ANOVA, and pearson's coefficient. While non-parametric just means that the distribution of does not follow a specific "standard" distribution, (ie a heavy tail when a test assumes normal). Tests for these are typically Wilcox rank sum and etc

Bias means underfitting. The model is too simple that it does not learn all the patterns in the training data. When using it in the testing dataset, it will miss several patterns. Variance means overfitting. The model learns even noise data, causing it to be sensitive to outliers during testing. The general pattern is impacted by noise in testing data.

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

As Program Manager my biggest achievement was leading the development of a complex electromechanical module for HP. The project consisted of developing and producing a huge electromechanical module for a high-speed large format printer.

I was in charge of overseeing the entire Product Development Process, from requirements gathering and solution design to development, testing, deployment, and mass production release.

The Key aspects of this project included

1.Strategic planning: I developed a plan including the project Objectives, scope, and deliverables, as well as timelines, milestones, and resource requirements.

2.Stakeholders management: I engaged with key stakeholders such as business leaders, external vendors, and customers to keep them all on the same page, ensure alignment, and address concerns.

3.Risk Management: Identifying potential risks and challenges associated with product performance, compatibility with external modules, material availability, etc.

4.Collaboration and Communication: I facilitated collaboration and communication among cross-functional teams involved in the project, including engineering, operations staff, and materials, to ensure alignment.

5.Quality Assurance: Implementing robust testing processes and quality assurance measures to ensure reliability and performance.

6.Mass production release: Worked with all operational teams to ensure material and factory readiness to launch the program to mass production

Finally, the project was released on time and satisfied all stakeholders, internal and customer. This reinforced the importance of clear and transparent communication between stakeholders to drive successful technical outcomes and foster a collaborative team environment.