Hi. I'm Daniel Quandt, and I work with data.

Casai is Mexico's premier hospitality startup, aiming to revolutionize hospitality in Latin America by providing hotel quality along with AirBNB locality and accessibility. As part of the Data Science team, I have had the opportunity to extract value from data in several areas of the company, including an automated pricing optimization tool, controlled experiments to verify business hypotheses and inventory automation.

A suite of tools for the film social media platform Letterboxd, including a random movie picker, film taste compatibility test and a fine-tuned recommendation system based on latent matrix factorization. Use of several intersecting technologies, including the Letterboxd API, Streamlit, FastAPI, Docker, and AWS ECS make this a great case study as a complete end-to-end ML app. Also, users love it!

As part of a four-person consulting team of engineers from VTEX and outside data scientists, I built a universal, store-agnostic recommender system that is able to create recommendations for any one of VTEX's 2500+ e-commerce stores. Our method uses adapted NLP techniques to generate recommendations through a neural network and outperformed the baseline we set by 350-500%.

As Distrito Dataminer's Data Lead, I created and managed all of the infrastructure and technology that allowed us to become the most comprehensive and in-depth resource on startup data in Brazil. I also explored the data to help make Dataminer a profitable area within Distrito through client work and generated novel analyses that made it so it was cited in the national media over 160 times in 2019.