I am passionate about leveraging the power of data and mathematical insights to optimize processes, products, and drive business growth. Currently, I am working as an Analytics Engineer & Business Analyst at XP Inc.
I hold a degree in Applied Mathematics with an emphasis on Scientific Computing from UFRJ. My interests include computation, algorithms, statistics, optimization, artificial neural networks, and machine learning. My undergraduate research involved connecting Neural Networks to Differential Equations through the development of hybrid models.

• Analytics Engineer & Business Analyst at XP Inc (07/2022 - Present)
• Data Analyst at Globo (12/2021 - 07/2022)
• Data Analyst at StormGroup (outsourced to Globo) (07/2021 - 11/2021)
• Full Stack (React & Django) and Data Analyst Intern at Voltalia (03/2021 - 07/2021)

During my undergraduate studies, I actively participated in scientific research. My main areas of interest include:

• Scientific Computing (algorithms)
• Statistics
• Probability Theory
• Regression and Classification models
• Fundamental theory of Neural Networks/Deep Learning
• Pure mathematics, such as Analysis and Theory of Linear Algebra

• And end-to-end MLOps Project: https://github.com/Schots/mlops_project

• Scientific Machine Learning: Adding Machine Learning to Differential Equations - Undergraduate Research
• Mathematical Methods on Geometric Mechanics - Undergraduate Research - https://github.com/mirandagil/geometric-mechanics
• An agent-based epidemics modelling on the city of Rio de Janeiro, being built with Python and Julia, to be published as a package - episiming
• Artificial Neural Networks and the problem of learning periodic signals - Research group

• myBlog My blog where I share some posts and insights about mathematics and computing
• NeuralNet.jl A Neural Network implemented from scratch in Julia
• ISLR Working through "Introduction to Statistical Learning" theory, examples, exercises and algorithm