Giovanna A.

Arvo is a healthcare analytics company focused on medical billing, claims auditing, and payment optimization between providers and payers.

• Designed and delivered generative AI solutions for medical billing and authorization workflows, using large language models (LLMs) to automate internal processes and accelerate decision-making.

• Built and deployed LLM-based agents with LangChain and RAG architectures, orchestrating multi-step pipelines integrated with APIs and relational databases.

• Developed and maintained production-grade analytics infrastructure, including data pipelines, feature engineering layers, data quality checks, and anomaly detection over large transactional healthcare datasets.

• Applied NLP and statistical methods to standardize medical terminology, improving downstream analytics consistency and model performance.

• Supported business stakeholders through dashboards, internal reports, and ad-hoc analyses, translating complex analytics into actionable operational insights.

Attribute detection in images for fashion retail using machine learning algorithms

• Technologies: AWS S3, Amazon SageMaker, AWS EC2, Google Cloud Platform, GCP Storage, Cloud run, Cloud functions, Docker, TensorFlow, fast API

Managed a team of twelve professionals, responsible for the fraud prevention strategy, models and study environment administration for banking and e-commerce transactions:

• Weekly Fraud Committee presentations with the main strategies and results;
• Daily monitoring of fraud KPIs;
• Daily monitoring and studying of new fraud heuristics performance;
• Build, Deploy and maintain Machine Learning Models for post-anomaly detection for the temporary detention of money in case of fraud;
• Build, Deploy and maintain machine learning predictive models for real-time fraud risk scoring;
• Maintain a useful Data Science environment as well as a Data Lake on AWS for studies and predictive model building;
• Daily monitoring of attacks in order to create rules to stop fraud in E-commerce, Face-to-Face Payments and Pagbank transactions.

Led a team of five Data (Scientist/Engineers) Analysts that:

• Rebuilt the "Online Game Payments Fraud Scoring", reducing losses related to frauds on a average of 30%;
• Built a predictive model for the new Payment API, marketed to online marketplaces;
• Built and deploy predictive models using various machine-learning tools for post anomaly detection for the temporary detention of money in case of fraud, recovering over 50% of a fraud chargeback;
• Test new data sources, measuring the costs (in case of being external) and benefits of new information;
• Study and create new features to improve real-time fraud scoring;
• Set up and maintain a Data Science environment and a Data Lake on AWS.

• Migrate the entire Payment Workflow to a new environment, obeying its limitations and maintaining the previous results;
• Build and deploy predictive models using various machine learning tools for real-time fraud prevention on e-commerce payments;
• Design models to detect anomaly in face-to-face payments, allowing retention and recovery of chargebacks related to crimes;
• Explain complex modelling in an understandable and relatable way;
• A/B Tests to create heuristics to prevent new frauds;
• Credit Scoring Modeling for P2P Loans.

• Migrate the entire Payment Workflow to a new environment, obeying its limitations and maintaining the previous results;

• Build and deploy predictive models using various machine learning tools for real-time fraud prevention on e-commerce payments;

• Design models to detect anomaly in face-to-face payments, allowing retention and recovery of chargebacks related to crimes;

• Explain complex modelling in an understandable and relatable way;

• A/B Tests to create heuristics to prevent new frauds;

• Credit Scoring Modeling for P2P Loans.

• Development of a Machine Learning Model for Credit Scoring for Auto Loan;

• Development of income Prediction Model;

• Responsible for the rollout of the Auto Loan Model;

• Data Analysis on the Hadoop ecosystem to test the current environment.

• Creation of predictive indicators of default;

• Responsible for elaborating on the monthly presentation of the credit portfolio situation to the bank’s CEO;

• Automation of periodic macroeconomic and default reports to the Credit Committee.