Camila Gonzalez
Investment Banking Associate
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolCamila Gonzalez is an investment banking professional with eight years of experience advising on complex transactions at Goldman Sachs and J.P. Morgan, now serving as an Associate in New York. She blends a quantitative foundation—dual BS studies in Industrial Engineering and Physics—with hands-on financial deal execution across M&A and capital markets. Beyond banking, Camila contributes to open-source data science work, notably improving time-series forecasting features in the popular darts Python library, signaling strong analytical and coding chops uncommon in traditional IB roles. Comfortable translating data-driven insights into client-ready recommendations, she brings a rare mix of technical fluency and transaction-level finance experience.
8 years of coding experience
Bachelor of Science - BS Industrial Engineering, Bachelor of Science - BS Industrial Engineering at Georgia Institute of TechnologyBachelor of Science - BS Physics, Bachelor of Science - BS Physics at Oglethorpe UniversityHigh School Diploma, High School Diploma at Colegio Karl C. ParrishBusiness/Managerial Economics, Business/Managerial Economics at Boston University
Stackoverflow
104reputation
26kreached
3answers
0questions
Github Skills (13)
forecasting10
machine-learning10
time-series10
forecast10
python10
arima10
pandas9
anomaly-detection8
deep-learning7
legend6
apache-spark6
pyspark6
matplotlib6
Programming languages (9)
Github contributions (5)
unit8co/darts
Jan 2021 - Mar 2021
A python library for user-friendly forecasting and anomaly detection on time series.
Role in this project:
Data Scientist
Data ScientistContributions:16 reviews, 10 commits, 23 PRs in 1 month
Contributions summary:Camila contributed significantly to the `darts` library, which focuses on time series forecasting and anomaly detection. Their work involved fixing issues related to time series generation, improving the readability of example notebooks, adding support for exogenous variables in ARIMA and AutoARIMA models, and integrating the RegressionEnsembleModel example. Furthermore, they made substantial changes to existing time series splitting functions and updated the library's dependencies.
forecastingpython-libraryanomalypythontime-series-analysis
camilaagw/fastapi-demo
Feb 2021 - May 2021
Contributions:48 commits, 2 PRs, 13 pushes in 2 months
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