Data Scientist
2019 - PRESENTZeeMaps (via Toptal)- 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.
Technologies: Bootstrap, PostgreSQL, Redis, Plotly, IPython Notebook, Docker, SaaS, Dash, Python, Data ScienceMachine Learning and Optimization Leader
2019 - 2020MercadoLibre (with Mutt Data)- 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.
Technologies: Docker, Machine Learning, IPython Notebook, Optimization, Programming, Prophet ERP, Pandas, Scikit-learn, Forecasting, Data Science, PythonMachine Learning Engineering Leader
2015 - 2019Jampp- 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.
Technologies: Pandas, Scikit-learn, Tornado, C, Presto DB, PostgreSQL, Bash, Git, Amazon Web Services (AWS), Linux, PythonResearcher | Developer
2013 - 2014Integrative Neuroscience Lab- 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.
Technologies: Emotiv SDK, Python