I am a data scientist with 4 years of end-to-end modeling experience, and 7+ years of financial industry experience.I have a passion for leveraging the power of machine learning to generate insights from data and solve business challenges effectively. Additionally, I find joy in continuously staying updated on cutting-edge machine learning techniques and sharing my learnings with colleagues to elevate the team’s productivity.• Data Science Tools: Python (pandas, scikit-learn, matplotlib), SQL, Scala, Jupyter notebook, Tableau• Cloud Platforms & Big Data: Snowflake, Amazon Web Services (AWS), Spark• Software Engineering Tools: Version Control (Git), Linux (bash, cron jobs), Docker containersAs an investment engineer at Bridgewater:• Wrangled equities time series dataset of 200 million rows (crucial to CIO-sponsored modeling initiative). Ensured data integrity and alignment with business needs by correcting 1+ million rows, and transformed & engineered features.As a data scientist at Fidelity:• Upgraded, documented, deployed, and monitored 2 machine learning models, which received a strong endorsement from the Model Risk Team, helped traders to price thousands of bonds daily, and reduced costs by outperforming third-party price vendors.At RBC Capital Markets:• Improved trading revenues by 10% by better understanding drivers of yields, via Lasso regression.• Lowered hedging costs by 25% by reducing portfolio dimensionality and redundant trades, via PCA.My strong foundation in data science began with STEM degrees from Cornell and Carnegie Mellon as well as a portfolio of end-to-end projects from the NYC Data Science Academy.I explored the world during 2018-2019, volunteered as an English teacher, and became fluent in Spanish from zero. I am currently studying Italian and Portuguese in my free time. I also love meditation, weight lifting, and reading about anthropology, history, and psychology.Please contact me at Joe.Zhou.Lu@gmail.com