Can you tell me about a project you are proud of?

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Anonymous · Accepted Answer

Project: One project I’m particularly proud of is a Python-based trading system
I developed for pairs trading. This market-neutral strategy aimed to exploit
pricing inefficiencies between two correlated stocks by taking long and short
positions based on daily stock returns.
Why I’m proud: Building the system from scratch was a challenging but rewarding
experience that required me to apply my programming and statistical analysis
skills in a real-world context. One of the main challenges I faced was
identifying stock pairs with high correlations while ensuring that the strategy
remained robust across different time periods. I overcame this by conducting
extensive backtesting, using 4.5 years of in-sample data and 6 months of
out-of-sample data, to validate the system’s performance. The results were
promising, even though I hadn’t factored in transaction costs yet, which
provided valuable insights into the strategy’s potential.
Additionally, this project pushed me to deepen my understanding of quantitative
finance, especially in areas like risk management and statistical modeling.
While I worked independently on most of the system, I also consulted with a few
peers for feedback on the model's assumptions, which improved its overall
design. This project not only reinforced my technical skills but also enhanced
my problem-solving and critical thinking abilities, making me more confident in
tackling complex financial models.
In summary, this project was significant because it challenged me to think
critically, apply technical skills in a practical setting, and collaborate with
others for feedback—all of which I believe are highly relevant to the role I’m
applying for.

Anonymous · Answer

Im most proud of one of my recent projects - architecting an event-driven system
for caching analysis outcomes, building a microservice to vend the outcomes
while also migrating existing data from DynamoDB to OpenSearch.
Reasons:
1. The existing data model was limiting in that it was not extensible to cache
other NLP analysis  outcome except translation for which it was built initially.
This made the system not usable for other newer use cases. I designed a new data
model that was generic and extensible to other analysis and allowed caching of
other results
2. Opensearch was a technology unfamiliar to our team and it involved a steep
learning curve for me and other people who worked on the project. I made sure I
documented any new learnings/choices so it was easier for the other team members
to pick up and work on it. I setup separate sessions with the other 2 engineers
who worked on it to go over some details like VPC, how private links are setup
etc to unblock them
3. I setup CI/CD pipelines and automated testing.
4. Setup metrics and alarms through Infrastructure as code
Result:
We were able to launch the project within tight deadlines

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