Keywords
HTML
JavaScript (Programming Language)
Agile Methodology
Amazon Elastic Compute Cloud
Cascading Style Sheets (CSS)
DevOps
Python (Programming Language)
PostgreSQL
MySQL
NumPy
Skills
Agile, EC2, backend, CSS, Celery, DevOps, Docker, FastAPI, Flask, HTML, Javascript, MySQL, NumPy, Pandas, Plotly, Postgres, Python, Pytorch, SQL, SQLAlchemy, SciPy, Scikit-Learn, Scrum, Selenium, TensorFlow, Wireframing
Project history
02/2022
-
Present
Led the tech team that designed, launched and iterated on the cross-platform app that employees can use to access their earned wage.
AKU
(< 10 employees)
Banks and financial services
Designed, coded and maintained microservices that talk to the cross-platform app, charge users' debit cards, connect to Mexican payment system, automate tasks, and execute bots. I was in charge of coding the backend in Python and deploying it as a FastaAPI app in a Docker container on an EC2 instance on AWS. I also implemented CICD using webhooks to pull the code from Git automatically on every merge.
05/2019
-
01/2022
Developed and trained a predictive classification model for sentences in macroeconomic research reports.
Swiss National Bank
(1000-5000 employees)
Banks and financial services
Coded the internal web app (HTML + CSS + Javascript) that would parse a PDF of a macro research report, extract all the sentences, and assign to each one of them a probability of being "relevant". Deployed on an Ubuntu server using NGINX as a reverse proxy. I built the model using a pre-trained BERT for encoding the sentences and a Vector Support Classifier for classification. The entire backend was written in Python.
03/2015
-
01/2022
Managed a model suite for forecasting short-term GDP growth in major world economies.
Swiss National Bank
(500-1000 employees)
Banks and financial services
Some of the most relevant models I developed and enhanced were Dynamic Factor Model, Mixed-Frequency Sampling model, a medium-size Bayesian VAR, and machine learning techniques for trees (most notably, XGBoost). Building and training these models made me learn a ton about data analysis. The data ranged from hard economic data (e.g. Industrial Production) to surveys (e.g. Purchase Manager Index) to financial data (e.g. stock prices) and big data (e.g. satellite images). The codebase was written in Python, MATLAB, and Eviews.
Local Availability
Only available for remote work
