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Angelo Oliveira is a highly experienced and accomplished Data Scientist and Engineer with an impressive network of 14,720 LinkedIn connections. With over 7 years of expertise, Angelo has developed innovative solutions integrating Data Science, Data Engineering, Machine Learning, and Cybersecurity. They have a strong track record of helping security teams monitor, detect, identify, understand, and prevent cyber threats, data exfiltration, and frauds by constructing and applying data-driven intelligent models and algorithms. Angelo's main skills include Data Science, Statistics, Machine Learning, Deep Learning, Cloud computing, Big Data, Data Engineering, Software Engineering, SIEM, anomaly detection, threat hunting, data exfiltration detection, fraud detection, and detection engineering. They hold a PhD in Informatics with a focus on Malware detection using Multimodal Deep Learning and have certifications in OSCP, OSCE, GCP Data & Database Engineer, and Splunk Power User. Angelo is also an international speaker at prestigious conferences such as Black Hat SecTor, DEATHCon, and INSOMNI'HACK and has demonstrated technical mentorship and leadership skills. They are a spare time inventor, currently working on an AI-based data exfiltration detector with a pending patent. Angelo is currently employed as the Staff Cybersecurity Data Scientist & Engineer at Mercado Livre Brasil, based in San Jose, California, and has previous experience at csds.tech, CompTIA, XP Inc., and Uninove - Universidade Nove de Julho.
More about this expert
- Full name
- Angelo Oliveira
- Location
- San Jose, California, United States
- Title
- Staff Cybersecurity Data Scientist & Engineer
- Company
- Mercado Livre Brasil
- Industry
- Wellness and Fitness Services
- LinkedIn profile
- https://www.linkedin.com/in/angelo5d0/
- LinkedIn Connections
- 14720
- Summary
- As a seasoned Data Scientist & Engineer, I bring more than 7 years of expertise in developing innovative solutions integrating Data Science, Data Engineering, Machine Learning, and Cybersecurity. I help security teams in monitoring, detecting, identifying, understanding, and preventing cyber threats, data exfiltration, and frauds by constructing and applying data-driven intelligent models and algorithms. Main skills: Data Science, Statistics, Machine Learning, Deep Learning. Cloud computing, Big Data, Data Engineering, Software Engineering, SIEM. Anomaly detection, threat hunting, data exfiltration detection, fraud detection, detection engineering. Defensive and Offensive security research. Reverse engineering, malware analysis, Windows internals. PhD in Informatics: Malware detection using Multimodal Deep Learning. Certifications: OSCP, OSCE, GCP Data & Database Engineer, Splunk Power User. International speaker: Black Hat SecTor, DEATHCon, INSOMNI'HACK. Technical mentorship and leadership. Spare time inventor: AI-based data exfiltration detector (patent pending). Google Scholar: https://scholar.google.com/citations?user=p7qUHiEAAAAJ&hl Playing the bass guitar!
- Skills
- Data Science Cybersecurity
Education
- Uninove - Universidade Nove de Julho
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Doctor of Philosophy - PhD (Summa Cum Laude)
Attended in Mar 2019 - Mar 2022
Field of study: Informatics - Universidade Presbiteriana Mackenzie
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Master of Science - MS
Attended in 2007 - 2008
Field of study: Electrical and Electronics Engineering
Positions
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Mercado Livre Brasil
São Paulo, São Paulo, Brazil · Remote
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Staff Cybersecurity Data Scientist & Engineer
Nov 2022 - Present
As a Staff Cybersecurity Data Scientist & Engineer, I've been working with teams of Data Analysts, Data Scientists, and Data Engineers to architect, engineer, develop, integrate, and operationalize intelligent data-driven solutions for insider threat detection, insider data exfiltration detection, and fraud detection. In less than a year, we operationalized and are managing:
* A platform for insider threat detection using behavioral data extracted from several security monitoring agents from Storages, Databases, APIs, and from IAM logs and SIEM.
As a result, we've been increasing our security posture, making it possible to detect, respond, and prevent previously unknown insider threats.
* A platform for insider data exfiltration detection using behavioral graphs built using data extracted from key sensitive internal systems.
As a result, we integrated custom insider data exfiltration alerts to SECOps and have been increasing our security posture, making it possible to detect, respond, and prevent previously unknown insider data exfiltration attempts.
* A platform for fraud analytics and fraud detection based on behavioral graphs built using data extracted from key sensitive internal systems.
As a result, the Internal Accounts Fraud Team has been leveraging the platform to monitor, detect, and understand the behaviors associated with our internal users and establish more effective controls resulting in a greater security posture towards insider fraudsters.
** Tech stack: Google Cloud Platform-based Data Engineering stack - PubSub, Cloud Storage, Dataproc (PySpark), BigQuery, Composer (Apache Airflow), Cloud Functions, Cloud Run. AWS-based Data Engineering stack - Kinesis Streams, Lambda, S3, Glue ETL (PySpark), EventBridge, Redshift. Data Science & Machine Learning stack: scikit-learn, PyTorch Geometric, Optuna, HDBSCAN, Isolation Forest, Random Forest, Transformers, Variational Graph Autoencoders, Graph Attention Neural Networks, GNNExplainer, SHAP.
- csds.tech
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Founder and Chief Nerd Officer
Jan 2020 - Present
As the Founder and Chief Nerd Officer (CNO) of csds.tech, I've been working on providing specialized consulting services on building customized solutions for internal threat detection such as malicious users, malware infiltration, and data exfiltration by employing state-of-the-art Artificial Intelligence (AI) algorithms, Data Science, and Data Engineering.
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CompTIA
Chicago, Illinois, United States
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CompTIA PenTest+ Subject Matter Expert (SME)
May 2018 - Present
During the "Cut Score Workshop" SMEs helped to determine and set the passing standard for the certification exam, and created, developed, and reviewed items for the CompTIA PenTest+ exam.
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XP Inc.
São Paulo, São Paulo, Brazil · Remote
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Senior Cybersecurity Data Scientist & Engineer
Nov 2021 - Nov 2022
As a Senior Cybersecurity Data Scientist & Engineer, I've helped XP to build its next-generation real-time security analytics platform. More specifically, I've architected, engineered, developed, and operationalized solutions to help:
* The SECOps Team to improve the alert triaging process using Machine Learning and Deep Learning based solutions to learn patterns in the alerts and their correlated events in order to estimate a prioritization ranking representing the probability of a given alert to generate a security incident.
As a result, the KPIs Mean Time To Detection (MTTD) and Mean Time To Response (MTTR) decreased in more than 30%. In addition, there was a decrease of more than 35% in false alerts (false positives). This initiative helped the SOC to become more agile and assertive.
* The SECOps teams to monitor, detect, identify, and prevent API abuses and abusers such as malicious users, bots, and attackers, using a Deep Learning Graph-based model to learn the patterns associated to normal and anomalous APIs usages.
As a result, the Detection Engineering Team was able gain insights on how to implement additional static rules to prevent API abuses and bots from scraping data from Stock Market APIs, and use the models' outputs to enrich the data used for decision making in more complex the detection rules.
** Tech stack: Google Cloud Platform-based Data Engineering stack - PubSub, Cloud Storage, Dataproc (PySpark), BigQuery, Composer (Apache Airflow), Cloud Functions, Cloud Run. Data Science & Machine Learning stack: scikit-learn, PyTorch, PyTorch Geometric, Optuna, HDBSCAN, Isolation Forest, XGBoost, Transformers, Graph Neural Networks, GNNExplainer, SHAP.
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Uninove - Universidade Nove de Julho
São Paulo, Brazil
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Graduate Research And Teaching Assistant
Jan 2019 - Mar 2022
As Research and Teaching Assistant, I've worked on the research and development of a novel malware detection and classification methods using Data Science, Machine Learning, and Deep Learning. For more information see "Education" and "Publications". In addition, I've lectured on Mathematics for Machine Learning, Machine Learning, Data Science, and Cybersecurity advanced topics. Finally, I've mentored MSc and PhD students, and reviewed research papers.