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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Portfolio of Bruno Silva Sette, Data Scientist.">
<title>Bruno Silva Sette | Data Scientist</title>
<style>
@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
body {
font-family: 'Roboto', sans-serif;
margin: 0;
padding: 0;
background-color: #f4f4f4;
color: #333;
}
header {
background: linear-gradient(135deg, #4a90e2, #50b0c4);
color: #fff;
padding: 40px 20px;
text-align: center;
position: relative;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
header .logo {
width: 60px;
height: 60px;
border-radius: 50%;
background-color: #fff;
display: inline-block;
margin-bottom: 15px;
background-image: url('https://via.placeholder.com/60'); /* Substituir por um ícone ou logotipo */
background-size: cover;
}
header h1 {
font-size: 2.5em;
margin: 10px 0;
font-weight: 700;
letter-spacing: 1px;
}
header p {
font-size: 1.2em;
margin: 0;
font-weight: 400;
opacity: 0.85;
}
.container {
max-width: 800px;
margin: 40px auto;
padding: 20px;
background-color: #fff;
box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
h2, h3 {
color: #333;
}
.section {
margin-bottom: 20px;
}
footer {
text-align: center;
padding: 20px;
background-color: #333;
color: #fff;
margin-top: 40px;
}
</style>
</head>
<body>
<header>
<h1>Bruno Silva Sette</h1>
<p>Data Scientist</p>
</header>
<div class="container">
<section class="section">
<h2>Profile</h2>
<p>I'm a developer for 13 years with a background in Data Science and a Master in Machine Learning. Extremely motivated, constantly developing my skills and professional growth.</p>
</section>
<section class="section">
<h2>Work History</h2>
<h3>Data Scientist Intern | AMERICANAS S.A</h3>
<p>2019 - 2021</p>
<ul>
<li>Developed AutoML solutions using the Apache Marvin AI framework, streamlining the automated search for optimal machine learning models.</li>
<li>Conducted extensive research on Automated Machine Learning (AutoML) and Neural Architecture Search (NAS), focusing on enhancing model efficiency and accuracy.</li>
</ul>
<h3>Data Scientist | AMERICANAS S.A</h3>
<p>2021 - 2023</p>
<ul>
<li>Training Deep Learning models using MLOps tools such as Docker, Kubernetes and CI/CD, on large-scale datasets (over 10 million product titles and images), orchestrated by Airflow using Vertex AI Jobs to train and processed with Dataflow jobs.</li>
<li>Proven experience working in a multi-cloud environment, leveraging both Google Cloud and AWS.</li>
<li>Applied bottom-up approaches (H-DBSCAN) to hierarchical product classification, identifying and removing outliers from the training set of hierarchical deep learning models.</li>
<li>Researched hierarchical multi-modal classification techniques (combining text and image) for e-commerce product categorization.</li>
</ul>
<h3>Data Scientist | SOFTPLAN UNJ</h3>
<p>2023 - 2024</p>
<ul>
<li>Deployed NLP machine learning models to production using MLOps tools such as Docker, Kubernetes and CI/CD, ensuring scalable, reliable, and automated model serving pipelines in AWS ECS and clusters Kubernetes.</li>
<li>Implemented ETL processes for SQL Server, PostgreSQL, and Oracle SQL databases from the Brazilian judicial system, which operates on servers with different versions and distributions, physically located across the national territory.</li>
<li>Created and deployed an OCR PDF API using AWS Step Functions and AWS Lambda with AWS Text Extract.</li>
<li>Applied hierarchical clustering techniques for semi-supervised learning to build a taxonomy of processes for state Prosecutor Offices.</li>
<li>Led and mentored a team of 3 junior data scientists, sharing both academic knowledge of machine learning and practical technical expertise in NLP, OCR and ETL projects involving judicial data from the Brazilian judicial system.</li>
</ul>
</section>
<section class="section">
<h2>Education</h2>
<h3>Computer Network Technician | Serviço Nacional de Aprendizagem Industrial</h3>
<p>2012 - 2014</p>
<ul>
<p>Awarded as the best student among the technical courses in the class graduated in the second semester of 2014.</p>
</ul>
<h3>Bachelor of Computer Science | Universidade Federal de Mato Grosso</h3>
<p>2015 - 2019</p>
<ul>
<p>Highlighted participation in the CNPQ Scientific Initiation Program focused on NLP and hierarchical text classification in the domain of industrial patents extracted from the World Intellectual Property Organization (WIPO).</p>
</ul>
<h3>Master of Computer Science (M.Sc.) in Machine Learning | Universidade Federal de São Carlos</h3>
<p>2019 - 2021</p>
<ul>
<p>Conducted literature review and published advancements in the field of NAS through an ensemble approach to search spaces and model combination, demonstrating that dividing the space into different network configurations can be beneficial for final classification metric results.</p>
</ul>
<h3>PhD candidate in Computer Science | Universidade Federal de São Carlos</h3>
<p>2021 - 2025 (Ongoing)</p>
<ul>
<p>Research on hierarchical multi-label classification techniques and development of a <a href="https://github.com/sette/hmc_torch">framework</a> to advance hierarchical multi-label categorization metrics using deep learning with PyTorch.</p>
<p>Current implementations and studies focus on metric learning to achieve improvements in hierarchical multi-label metrics based on the research hypothesis.</p>
</ul>
</section>
<section class="section">
<h2>Contact</h2>
<ul class="contact-info">
<li><strong>Email:</strong> brunosilvasette@gmail.com</li>
<li><strong>Phone:</strong> (11) 95425-2654</li>
</ul>
</section>
</div>
<footer>
<p>© 2024 Bruno Silva Sette. All rights reserved.</p>
</footer>
</body>
</html>