Verified Expert in Engineering
Machine Learning Engineer and Developer
Santiago is a machine learning engineer keen on solving large-scale data, optimization, and engineering problems. He has led a team of data scientists to build products that predict the value of over 300 million users worldwide to optimize the advertisement of world-leading companies. Santiago considers it a serious undertaking when developing elegant, efficient, and well-designed software systems.
Bash, Vim Text Editor, Python, Git, Linux
The most amazing...
...machine learning system I've developed streams over 60 million messages per hour and builds datasets in real time to predict monetization on mobile apps.
ZeeMaps (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.
Machine Learning and Optimization Leader
MercadoLibre (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.
Machine Learning Engineering Leader
- 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 | Developer
Integrative 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.
Building Stronger Data Science Teams
For this reason, I started a series of on-site technical pieces of training in computer science and software engineering because the team's data scientist did not have an engineering or computer science background.
The training sessions were on topics like algorithmic complexity, programming exercises, environment setups, data structures, object-oriented philosophy, and software architecture.
On the tech side, this—in conjunction with proper onboardings and code reviews—led to better products and faster development times. On the social side, we were happy to find out that we were unexpectedly making a stronger team by the bonding that arose from studying and debating shared interests.
Dynamic Spend Model for Digital Marketing
I worked on a new spend model in which the client and the platform agree on a maximum budget and goals—such as a maximum cost per click or purchase—and the system finds the optimum price to charge the client while achieving the goals and ensuring satisfactory results for both parties.
Event Predictions in Mobile Applications
This was a challenging project as there were different types of apps (like food delivery or flight reservation apps), different types of events (like searches or purchases), and delayed conversion feedback (people usually do not book a flight right after watching an ad).
Before deploying it to production and using it for revenue optimization, it was important to answer a number of questions:
• What is the minimum amount of data required to fit a good estimator?
• What happens if you stop learning from the data and keep using your current estimator?
• How much are you willing to wait for delayed feedback?
• Of all the events in an app which one do you want to predict?
After working on the problem and considering the possible contingencies, the system was rolled out successfully without any issues.
Financial Education Mobile Game
The game consisted of a simulation of a rapper's career from the ground up, in which the player had to give concerts playing mini-games and take good decisions in order to progress in their career.
The player's manager helps him by giving him advice and suggestions to balance leisure time, taking loans for playing in bigger venues and buying swag to increase the avatar's flow. The project was presented at a contest organized by Itau Bank and Red Hat.
Scikit-learn, Pandas, REST APIs, NumPy, SciPy, Matplotlib, ZeroMQ, Keras
PostgreSQL, Redis, Amazon S3 (AWS S3), MySQL
Click-through Rates (CTR), Machine Learning, Tornado, Algorithms, Distributed Systems, Optimization, Vowpal Wabbit, SSH, Multiprocessing, Dash, SaaS, Forecasting, Programming, Image Processing
Presto DB, Flask, Bootstrap
Git, Docker Compose, Vim Text Editor, Emotiv SDK, IPython Notebook, Plotly, Prophet ERP
Linux, Docker, Jupyter Notebook, Amazon Web Services (AWS), Amazon EC2
Master's Degree in Computer Science
University of Buenos Aires - Buenos Aires, Argentina
Bachelor's Degree in Computer Science
University of Buenos Aires - Buenos Aires, Argentina