At Grupo Boticário, I lead the development of scalable back-end systems and optimized real-time data retrieval solutions. My work centers on designing and implementing high-performance Retrieval-Augmented Generation (RAG) systems that seamlessly integrate generative AI models, driving insights and enhancing data accessibility across the organization. Focused on maximizing value, I deliver robust data pipelines that ensure reliable, efficient, and impactful data-driven solutions for various business needs.Responsibilities- Collaborated with cross-functional teams to design, architect, and deploy scalable RAG systems, enhancing real-time information retrieval.- Developed and optimized GenAI solutions, including smart chatbots and recommendation engines, leveraging LLMs and prompt engineering to improve response relevance and user engagement.- Integrated vector databases (e.g., Pinecone, ElasticSearch) to enhance embedding storage and retrieval, driving increased accuracy and scalability in search and recommendation functionalities.- Built and maintained data pipelines to support high-volume data retrieval across internal and external systems, ensuring seamless and low-latency information flow.- Employed Docker and Kubernetes for containerized deployment of back-end services.- Collaborated with data science and engineering teams to create and deploy reusable libraries and frameworks, streamlining generative AI model integration with back-end systems.- Evaluated and incorporated advancements in generative AI and RAG technologies (e.g., LangGraph, LangFlow) to ensure innovative and future-proof AI solutionsTechnologies and Frameworks- Python, SQL/NoSQL, Node.js, TypeScript, LangChain, LangGraph, LangSmith, LangFuse, LangFlow, Pinecone, AstraDB, Supabase, ElasticSearch, Hugging Face Transformers, TensorFlow, PyTorch. GCP (Vertex AI), Microsoft Azure, AWS Bedrock, Databricks, Docker, Kubernetes, GitHub, Agile Framework.

At FortBrasil,I specialize in performance forecasting and predictive analytics. My role revolved around analyzing and interpreting performance indicators and financial data to discern trends and patterns crucial for business insights, leveraging data to provide robust forecasting and trend analysis solutions for the FP&A department, enabling informed decision-making and strategic planning.Responsibilities:- Analyze and understand performance indicators and financial data to identify trends and patterns- Develop and implement data-driven forecasting models to predict future performance metrics- Collaborate with FP&A team to translate business requirements into actionable insights- Clean, preprocess, and validate data to ensure accuracy and reliability in forecasting models- Utilize statistical methods, machine learning techniques, and predictive analytics to extract valuable insights- Engage in cross-functional collaboration to align forecasting efforts with organizational goalsHighlighted achievements:- Successfully delivered forecasting solutions for key performance indicators, leading to a 15% improvement in accuracy and efficiency- Implemented advanced forecasting methodologies resulting in a 25% reduction in forecasting errors and improved decision-makingTechnologies and Frameworks:- Python Forecasting Libraries (Exponential Smoothing, ARIMA, SVR, LSTM, XGBoost, Prophet), SQL, GCP, GitHub, SCRUM, Kanban

I worked as a Data Scientist specializing in demand forecasting and inventory management. My role involved analyzing data to uncover patterns and trends affecting product demand, collaborating with cross-functional teams to develop data-driven solutions, and overseeing the entire lifecycle of forecasting models. I provided actionable insights to optimize inventory management, reduce stockouts, and enhance operational efficiency.Responsibilities:- Conduct exploratory data analysis to identify patterns, trends, and relevant insights impacting product demand- Collaborate with multifunctional teams to comprehend business requirements and translate them into data science solutions- Manage the complete lifecycle of forecasting models, from data collection and preparation to evaluation and deployment in production- Provide actionable insights and data-driven recommendations to optimize inventory management and reduce stockouts in stores- Monitor the performance of forecasting models in production and make adjustments as necessary to ensure the accuracy and reliability of predictionsHighlighted achievements:- Improved demand forecasting accuracy by 25%, enhancing operational efficiency.- Reduced stockouts and inventory costs by 30% through data-driven insights.- Increased sales revenue by 20% and decreased excess inventory by 15%.Technologies and Frameworks:- Python R, PySpark, SQL, Airflow, Databricks, BitBucket, SCRUM, Kanban

As a Senior Data Analyst at Neoway, I played a pivotal role in driving the successful adoption and optimization of Neoway's Big Data solutions for clients such as Hapvida and Nacional Gás, with a focus on maximizing value and ensuring customer success.Responsibilities:- Strategized and executed tailored implementations of Neoway's solutions to meet the unique needs and objectives of each client.- Conducted in-depth data analysis using tools like Power BI and SQL to derive actionable insights from large datasets, translating findings into visually compelling presentations to facilitate decision-making.- Managed end-to-end project lifecycles, ensuring timely delivery within budgetary constraints and proactively addressing any obstacles.- Evaluated client ROI, demonstrating the tangible value and impact of Neoway's solutions on their business outcomes.Highlighted Achievements:- Implemented automation scripts and optimized SQL queries, reducing data processing time by 40% and enhancing efficiency in handling large datasets.- Enhanced performance by 50% through the review and optimization of existing Power BI dashboards.Technologies and Frameworks: - Python, SQL, Power BI

As a Senior Data Analyst at GD7 Distribuidor de Alimentos LTDA, my main focus was on creating analytical dashboards and forecasting sales for the commercial department.Responsibilities:- Extracted, transformed, and loaded data from various sources into our analytics platform to ensure data accuracy and consistency (ETL process).- Managed the entire data pipeline, ensuring smooth data flow from source systems to analytical tools like QlikView.- Conducted statistical analysis on sales data to identify trends, patterns, and anomalies influencing business decisions.- Developed and implemented forecasting models using Python and SQL to predict future sales trends, empowering the commercial team to optimize resource allocation.- Created visually appealing and interactive dashboards using QlikView, delivering actionable insights to stakeholders throughout the organization.Highlighted Achievements:- Improved efficiency by 10% through streamlining the ETL process with automation techniques.- Enhanced sales forecasting accuracy by 25% through the development and implementation of advanced forecasting models using Python and SQL.- Provided real-time access to key sales metrics and performance indicators through the design and deployment of interactive dashboards using QlikView.Technologies and Frameworks:- Python, SQL, QlikView

As a Data Analyst at Solar Coca-Cola, I worked within the Demand Planning department with a primary focus on optimizing consumption forecasts for industrial production.Responsibilities:- Contributed to the development and implementation of the sales and operations planning process (S&OP), ensuring alignment between sales forecasts and production schedules to meet demand effectively.- Participated in day-to-day execution of sales and operations plans, monitoring performance metrics and adjusting forecasts as needed to maintain optimal inventory levels and production efficiency.- Developed accurate sales forecasts based on historical data, market trends, and other relevant factors, providing critical insights for decision-making in production and inventory management.- Conducted in-depth data analysis using Python and SQL to identify patterns, trends, and correlations in sales data, enabling more accurate forecasting and strategic planning.- Created visually compelling dashboards and reports using PowerBI to communicate insights effectively to stakeholders across the organization, facilitating data-driven decision-making.Highlighted Achievements:- Implemented advanced forecasting models using Python, resulting in a 20% improvement in forecast accuracy- Played a key role in streamlining the Sales and Operations Planning (S&OP) and Sales and Operations Execution (S&OE) processes, leading to a 15% reduction in lead times and improved alignment between sales forecasts and production schedulesTechnologies and Frameworks:- Python, SQL, PowerBI