Data Scientist2019 - PRESENTZeeMaps (via Toptal)
Technologies: Bootstrap, PostgreSQL, Redis, Plotly, IPython Notebook, Docker, SaaS, Dash, Python, Data Science
- Analyzed the company's system-as-a-service (SaaS) data to find insights and make strategic business decisions.
- Built a dashboard to evaluate the company's evolution and easily see the analysis and aid decision making.
- Classified subscribed users into acquired and at-risk users—enabling us to focus on preventing risk users of churning, driving the company's growth.
- Provided visibility on the revenue, lifetime value, churn rate and other metrics per user category.
- Tagged users in high and medium churn risk to allow focused support and customer service.
- Analyzed and optimized sponsored search campaigns to acquire new users.
- Dockerized the setup and deployment of the dashboard system.
- Performed cost analysis per plan and per user to evaluate the current SaaS's pricing methodology and improve the revenue by performing deals with high consumers when appropriate.
Machine Learning and Optimization Leader2019 - 2020MercadoLibre (with Mutt Data)
Technologies: Docker, Machine Learning, IPython Notebook, Optimization, Programming, Prophet ERP, Pandas, Sklearn, Scikit-learn, Forecasting, Data Science, Python
- Defined an optimization problem to optimize the marketing's team budget and target ROAS (return on advertising spend) goal allocations revenue from Google Shopping.
- Performed an exploratory data analysis on the company's and the marketing's team data in order to understand the relationship between budgets, goals, and results.
- Developed a system to forecast and predict cost, revenue and return of investment of Google Shopping campaigns depending on the budget and target ROAS goal subject to business constraints.
- Engineered and developed a system to solve the optimization problem and define how to set up digital marketing campaigns on Google Shopping based on the predictions, forecasts and business constraints.
- Dockerized the setup and deployment of the system using docker containers and docker compose.
- Deployed the system for two accounts in a single country, after the results, on five more accounts on the same country, followed by rolling out to other countries.
Machine Learning Engineering Leader2015 - 2019Jampp
Technologies: Pandas, Scikit-learn, Tornado, C, Presto DB, PostgreSQL, Bash, Git, Amazon Web Services (AWS), Linux, Python
- Built machine learning online estimators processing over 60 million programmatic advertisement messages per hour (an auction's win rate, the second price auction's costs, and new user and in-app events conversions).
- Performed optimization and feature engineering on models leading to over 15% in conversions and a 30% increase in the company's net revenue.
- Developed a revenue and inventory purchase optimization system that resulted in over 20% increase in the net revenue.
- Created several web interfaces to provide visibility and interpretability to machine learning and optimization systems.
- Led data scientists to define and develop user clustering systems processing over 200 million users.
- Developed a system that uses the previously mentioned ones to make various decisions—like how much to bid and for which client—during real-time auctions selling advertisement slots on mobile apps.
Researcher | Developer2013 - 2014Integrative Neuroscience Lab
Technologies: Emotiv SDK, Python
- Created a mind speller in Python using Fisher’s LDA and SVMs compatible with the EMOTIV EPOC electroencephalography headsets.
- Worked on a relaxation-based competition game using alpha brainwave detection among players.
- Contributed to the development of a steady-state visually evoked potential selection interface to control Lego Mindstorm cars from afar.
- Built a web server to control the headsets, launch the different systems, and record the brain activity and user input to enlarge our datasets.
- Researched on mind-speller variants using different classification algorithms and signaling environments.