Gabriel Barros Lins Email & Phone Number

Brazil

Worldwide, Br

As a Data Scientist at Radix, I am at the forefront of developing advanced computer vision solutions to enhance industrial processes. My role encompasses the full data lifecycle, from acquisition to real-time model deployment, leveraging a wide array of cutting-edge technologies:* Computer Vision: Proficiency in YOLO, OpenCV, and Pillow for advanced image analysis and processing.* Cloud Computing: Extensive use of Azure services, including Azure Functions, Azure Computer Vision, and Azure Custom Vision, for scalable and efficient operations.* Model Development: Design and implementation of machine learning models for object detection, utilizing techniques such as transfer learning, finetuning, and deep learning frameworks like TensorFlow and PyTorch.* Data Management: Expertise in data ingestion, processing, and feature engineering using Cognite Data Fusion, as well as managing both relational (SQL) and non-relational (NoSQL) databases.* System Integration: Development of RESTful APIs and web services for seamless system integration and real-time presentation of results to factory operators.* Generative AI and Large Language Models: Integration of Generative AI and Large Language Models (LLMs) for automatic analysis of frames that triggered alerts, providing detailed insights to operators.* Retrieval Augmented Generation (RAG): Currently developing a RAG system to enable operators to easily access log and alert information, enhancing operational efficiency and decision-making.This project significantly reduces daily production losses, providing substantial economic benefits to the factory. It underscores the transformative power of combining computer vision, machine learning, data analytics, and generative AI to drive industrial efficiency and economic growth in Industry 4.0.

São Paulo, São Paulo, Br

I am a Data Scientist specializing in Natural Language Processing (NLP), with a strong focus on data handling, feature engineering, modeling, and the deployment of predictive NLP models. My toolkit includes a range of tools and technologies, such as Python, Pandas, Scikit-Learn, HuggingFace, Pytorch, TensorFlow, SQL, non-relational databases, Docker, Airflow, NLTK, Spacy, Transformers, Language Model Models (LLMs), LangChain, Vector Databases, and Azure.One of my achievements involved creating a binary question-answering model that boasts an impressive 89% accuracy and 85% recall. Furthermore, I have hands-on experience with language models (LLMs), where I have developed an agent capable of generating automated texts and performing data analysis tasks.

Natal, Rn, Br

Data Scientist and researcher in the field of Machine Learning, experience with healthcare data analysis, statistical analysis, development of machine learning models for diagnostic prediction, ETL, data cleaning, python, sql, pandas, scikit-learn, tensorflow, keras, pytorch, matplotlib, plotly, Spark, xgboost, lightgbm, catboost, streamlit, fastAPI, causal inference, association rules (exApriori), Deep Learning. We develop solutions with data to improve public health in Brazil. The work developed has a real impact on public health in the state of RN and Brazil, I work on the fronts to combat Syphilis, Covid and SUS audit.

I worked as a Data Scientist at Murph, where we offered data consulting services to healthcare organizations, such as clinics and hospitals. My role involved creating predictive models to address various challenges in the healthcare sector.I contributed to the development of predictive models for customer churn, helping identify patients who were likely to discontinue their treatment. Additionally, I contributed to the development of no-show models, which predicted whether a patient would miss or cancel their appointments.I also worked on propensity to purchase models, specifically focused on promoting packages of medical exams after a consultation. These models led to a significant increase in sales, boosting revenue by 27%.My ongoing work at Murph involves medical performance analytics, where we leverage data to assess how each doctor contributes to clinic revenue and evaluates their performance in each appointment. This data-driven approach enables us to optimize doctor schedules, ensuring that more productive doctors have appropriate time slots while providing support and resources to those in need of improvement.Throughout these projects, I utilized the latest data science tools and the AWS platform to extract, analyze, and derive insights from healthcare data.

Electroencephalographic data analysis using the Python language, experience with signal analysis and processing, time series, python language, data visualization tools (matplotlib, seaborn, plotly), statistical analysis, machine learning, pandas. The analyzes were performed with signals captured during sleep and wakefulness. I was able to generate insights from EEG data using statistical methods, data visualization tools and machine learning, and also developed an ocular pattern classifier (blinks and saccades) from electrooculography time series. I was part of the Sleep, Dreams and Memory laboratory coordinated by Professor Sidarta Ribeiro.

Bachelor'S Degree, Computer Engineering

Bacharelado Em Ciências E Tecnologia, Neuroscience

Technician, Electrical, Electronic And Communications Engineering Technology/Technician