Hi! My name is Guilherme Duarte Marmerola, and I’m a Data Scientist from Brazil.
I’m passionate about the power that data and decision science gives us to solve hard problems and drive business positive outcome in nearly every industry in existence.
I had the privilege to enjoy building and developing high performing data and decision science teams, shipping cool products and creating reliable decision making processes with machine learning and advanced statistical methods in several industries.
I hold a special interest about some specific topics:
Reinforcement learning and bayesian inference: particurlarly, I’m interested in the exploration-exploitation dilemma, and how to solve it with (bayesian) models that capture uncertainty. I’ve devoted a lot of time to the study of bandit problems, and how to use them as frameworks to fundamental problems in industry (pricing, resource allocation, recommendation, and others).
Dimensionality reduction and clustering: how can we visualize and interpret structure in our data, aiming to explain it to business stakeholders? Can we do better than simple K-Means clustering and PCA? What similarity measures, algorithms and representations would make this possible?
Causal Inference: in industry, a sizeable chunk of projects start when someone in your organization asks for your help answering what-if questions such as:
- what would happen if we lowered prices?
- if we recommended product A instead of B, would our revenue grow?
- if we sent e-mails to our customers instead of calling them, what would happen to our conversion rate?
All of these questions, no matter how varied, can be solved with causal inference methods. How can we go beyond Randomized Controlled Trials and A/B tests and extract value from observational data in these contexts?
Management, and data science in a business context: doing data science in industry is far from trivial. We need to balance time for experimentation with deadlines, data is oftentimes messy and stakeholders may not have a clear understanding of our work and challenges. How can you navigate this and deliver real value from ML, while maintaining the best people motivated?