André Brandão Email & Phone Number

Vancouver, Bc, Ca

Vancouver, Bc, Ca

Belo Horizonte, Minas Gerais, Brazil

Seedz is dedicated to optimizing the agribusiness ecosystem through solutions such as a loyalty program to enhance sales campaigns by offering rewards to sellers and cashback to customers. Operating across 10 countries, we have facilitated financial transactions worth over BRL 10 billion via our platform.I lead the Data Monitoring and Validation team (DMV), which ensures the quality and integrity of data across the entire pipeline—from ingestion to availability for our system consumption, evaluating existing technology environments to identify and address critical such as operational, financial, and security issues. My team designs and implements solutions while integrating new systems in alignment with architecture standards. In addition to troubleshooting technical issues, we support Product, Business, and Operations teams in fraud detection, client segmentation, and identify inefficient business rules. Our work encompasses Data Analysis, Process Mining, Machine Learning, Clustering, Data Visualization (dashboards), and the development of Webhooks and REST APIs.Achievements:■ REST API services to generate comprehensive reports on data quality, revenue, and loyalty scores, enabling improved business insights■ built solutions to ensure data quality from ingestion to consumption■ conducted analyses to improve sales campaign frameworks, enhancing rewards for sellers and cashback for customers■ mentored and guided technical teams, establishing best practices in architecture and frameworks that align with the business goal■ dashboards that enable Operations teams to quickly identify and address issues like reward failures, fraud, and inefficient business rulesTools: AWS, Azure DevOps, Docker, Git, Python, Pandas, Spark, Statistics, Matplotlib, Plotly, Metabase, Scikit-Learn, Jupyter, Streamlit, Flask, Tornado, Swagger, AsyncIO, Celery, Alembic, Sqlalchemy, MySQL, Postgres, Redshift, MongoDB, DynamoDB, Redis, SQS, EC2, ECS, EKS, CloudWatch, S3, Lambda

São Paulo, Brasil

Leading a data science team that supports Product and Business teams in boosting the effectiveness of agribusiness companies. We performed Data Analysis, Process Mining, Machine Learning, Clustering, Data Visualization (e.g., Dashboards). We also developed Webhooks, REST APIs, and pipelines to extract, transform, and load data (ETL).Achievements:■ web crawlers to systematically collect public data from governmental organizations, property listings, stock exchange, and others■ a machine-learning model utilizing Deep Learning techniques to predict soybean yields to well-known cities in Brazil for its exceptional soybean productivity■ dashboards reporting insights to assist banks and tradings in compliance analysis and business plans■ REST API service to deliver property valuations based on location. To achieve this, we implemented a web crawler to systematically gather pricing and property features data. Following data collection, we conducted in-depth data analysis and developed a robust machine-learning model that produced highly accurate results. As a result, our solution drastically cut the property valuation time from 8-10 days to a few minutes and successfully integrated into a major Brazilian bank's credit provision pipeline for growers■ statistical, process mining methods, and dashboards that enabled Product teams to identify user interaction patterns and preferences, event flows, pinpointed bottlenecks, underutilized pages, and clustered users based on their behavioral patterns. This initiative significantly enhanced the user experience and contributed to improved user retention on the platformTools: AWS, Azure Devops, Jenkins, Argo CD, Docker, Git, Python, Pandas, Spark, Numpy, Pm4py, Statistics, Matplotlib, Plotly, Metabase, Scrapy, Scikit-Learn, Jupyter, Streamlit, FastAPI, Tornado, Flask, Swagger, AsyncIO, Pika, Alembic, Sqlalchemy, MySQL, Postgres, MongoDB, RabbitMQ, EC2, ECS, CloudWatch, S3, Lambda, Athena, Glue, DMS, Lake Formation, EMR

Rio De Janeiro, Brasil

Teaching basic knowledge of programming, data analysis, and data visualization for a Data Analyst course. This project was performed in partnership with Ifood, a food delivery company.Achievements:■ created content of programming, data analysis, database management, data visualization, dashboards, and storytelling used by over 60 studentsTools: Python, Git, Docker, Colab, Pandas, Numpy, Statistics, Matplotlib, Seaborn, Postgres, MongoDB, DynamoDB, Tableau, PowerBi

Rio De Janeiro, Brasil

Conducted data analysis and exploration of data from Petrobras, a major Oil and Gas company in Brazil, focused on identifying patterns, and developing prediction models and dashboards to enhance operational efficiency.Achievements:■ The company faced an issue of inaccuracies in reports of equipment inspection performed by experts. Our goal was to build an artificial intelligence model to assist inspectors in report writing. We constructed a comprehensive database of inspection reports and performed data analysis. Then, we developed a web service using a machine learning model to detect inaccuracies or gaps in reports and suggest appropriate terms for damages, causes, mechanisms, and actions. As a result, the harmonic mean of accuracy and precision (F1 score) reached a result greater than 95%. This project received the Petrobras Inventor Award 2022 in recognition of the results achieved and the impact on the efficiency of equipment maintenance in refineries.■ Petrobras faced a critical issue with elevated hydrogen sulfide (H2S) emissions in its oil refineries, posing a severe threat to the safety of the local community. To assist the refineries in identifying and mitigating the root causes of elevated H2S emissions. We conducted in-depth data exploration, correlating refinery historical data with meteorological information. Subsequently, we developed a robust machine-learning model to provide prescriptive insights into H2S emissions. A Power BI dashboard was also created to visualize the factors influencing H2S levels every 5 minutes. Our solution encompassing machine learning and a dashboard led to a substantial reduction in H2S emissions and was successfully deployed across multiple refineries.Tools: Azure, Azure DevOps, Azure Functions, Docker, Git, Python, Pandas, Numpy, Statistics, Matplotlib, Seaborn, Plotly, Scikit-Learn, PyTorch, Jupyter, Flask, Swagger, MySQL, SQL Server, MongoDB, Git, Power Bi

Rio De Janeiro

Analysis and exploration of data from Public Prosecution and Court of Law of Rio de Janeiro to increase the efficiency of the legal processes. I was responsible for conducting Data Analysis, Data Visualization, and Machine Learning modeling as natural language processing (NLP) to identify patterns in the court proceedings.Achievements■ Prosecutors faced challenges in identifying legal texts with similar contextual content within the vast repository of historical documents. Our goal was to develop a service that empowers prosecutors by providing efficient assistance in identifying legal texts with similar content, enhancing their workflow. We conducted a data analysis of the historical documents from the Public Prosecution, employing data analysis techniques to extract and explore information from PDF files. Utilizing a pre-trained model with word embeddings, we built a service that retrieves similar texts through vector representations, employing cosine distance calculations and sorting the results accordingly. Our solution significantly improved the efficiency of document retrieval for prosecutors. It was successfully deployed enhancing the overall workflow and effectiveness of legal text analysis■ I led a significant project to enhance the operational efficiency of the Public Prosecution in Rio de Janeiro by optimizing the allocation of prosecutors to individual legal cases. This involved analyzing the legal journey of lawsuits from the Court of Law to the Public Prosecution. After cleaning the data and identifying correlations within the dataset, we developed a machine learning model that could automate the matching of the most suitable prosecutor to each lawsuit. We then deployed this model as a service, seamlessly integrating it into the Public Prosecution's workflow and improving the lawsuit allocationTools: Docker, Git, Python, Pandas, NumPy, Statistics, Scikit-Learn, Seaborn, Matplotlib, Plotly, Tableau, Flask, AsyncIO, Jupyter, MySQL, MongoDB

Rio De Janeiro E Região, Brasil

Working on the official Ginga Middleware development project, which is the middleware of the Japanese-Brazilian Digital TV System (ISDB-TB) and ITU-T H.761 recommendation for iPTV services. Achievements:■ fixed reported bugs, and refactored the source code to improve its efficiency and memory usage■ improved the video player used by Ginga to support operations of resize and freezing screen, setting audio, etc.Tools: C, C++, Lua, GStreamer, NCL, GDB, Git, XML, Shell script, JavaScript, MySQL, MongoDB

São Luís E Região, Brasil

Leading a team responsible for developing an authoring tool for multimedia Learning Objects (LOs) to allow teachers to create multimedia educational content for interactive TV and the Web without requiring programming skills.Achievements■ developed Cacuriá, a desktop tool to support instructors in creating multimedia educational content for TV and Web, used by 13 universities■ integrated Cacuriá with the iVoD (Interactive Video on Demand) service from RNP - a National Research and Educational Network responsible for promoting the development of networks in Brazil ■ launched Mestrar, an online platform to store and deliver Learning Objects created by Cacuriá■ conducted courses to disseminate the Cacuriá in Brazil.Tools: C, C++, Qt, Lua, NCL, GDB, Git, XML, Shell script, PHP, JavaScript, MySQL

Ufma

Working on the Cacuriá development project, an authoring tool for multimedia Learning Objects (LOs) to allow teachers to create multimedia educational content for interactive TV and the Web without requiring programming skills. Cacuriá is integrated with the iVoD (Interactive Video on Demand) service from RNP, a National Research and Educational Network responsible for promoting the development of networks in Brazil, including the development of innovative applications and services.

São Luís, Brasil

Working on the development and support of a system to manage municipal tributes used by 17 cities from Maranhão, Brazil.

São Luís

Working on the development and support of a system to manage municipal tributes used by Brazilian cities.

Ufma

Research and Developing NCL applications to be run on Ginga Middleware, which is the middleware of the Japanese-Brazilian Digital TV System (ISDB-TB) and ITU-T H.761 recommendation for IPTV services. Presenting workshops once a semester to present research results, as well as obtaining professors' feedback from UFMA. Promoting events of coding on Robocode, where the goal is to develop a robot battle tank to battle against other tanks in Java. Besides, we conducted courses, such as "Introduction to HTML", and "Development of NCL applications". The audience of these courses were students from UFMA, as well as people outside UFMA.

Doctor Of Philosophy - Phd, Informatics

■ Advisor: Simone D. J. Barbosa ■ Activities: Focused on the Data Science area, working on models for Visual Learning Analytics to support.

Master'S Degree, Computer Science

■ Advisor: Carlos de Salles Soares Neto ■ Activities: Working on the Cacuriá development project, an authoring tool for multimedia.

Bachelor Of Science - Bs, Computer Science

■ Advisor: Carlos de Salles Soares Neto ■ Site: http://deinf.ufma.br/