Data Scientist with over 3 years of experience in Data Science, Machine Learning, and Analytics.
Data Scientist with over 3 years of experience in Data Science, Machine Learning, and Analytics. Proven ability to develop and implement predictive models using Python, R, and SQL, driving strategic insights in healthcare, market research and banking sectors. Successfully delivered projects for Medical Innovation, Research and Development, Digital Health, and IT areas, achieving significant cost savings, revenue growth and process improvements. Adept at integrating multidisciplinary teams to align business strategies and foster digital transformation. Committed to delivering innovative solutions that boost organizational growth and efficiency.
Analytical dashboard focused on inferential statistics. The application performs hypothesis tests (ANOVA and T-test) and real-time correlation analyses to determine the statistical significance of factors such as sleep, frequency, and study hours on students’ final performance.
Tools used:
R, Shiny, ggplot2, dplyr, and tidyverse.
Interactive dashboards with Shiny.
Exploratory Data Analysis (EDA)
Development of an interactive web application for exploratory analysis of nutritional data from various cereal brands. The dashboard enables comparison of key nutritional metrics such as calories, sugars, sodium, and protein across manufacturers.
Tools used:
R, Shiny, ggplot2, dplyr, and tidyverse.
Interactive dashboards with Shiny.
Exploratory Data Analysis (EDA).
Application of Generative AI to unstructured MRI and CT scan reports to build a patient journey dashboard for spine orthopedics within Digital Health. The solution enabled strategic decision-making for Primary Care and Digital Emergency services, resulting in R$3.9 million in additional revenue through optimized resource allocation and an 11% improvement in patient retention.
Tools used:
Python, Vertex AI, Gemini API, Scikit-learn, Pandas, NumPy, Multiprocessing, JSON, and Seaborn.
Google Colab and Google Cloud Platform.
Prompt Engineering.
Looker Studio.
GitLab.
Airflow.
This project aimed to develop a Machine Learning model using Convolutional Neural Networks (CNNs) to classify chest radiography images and predict the Agatston coronary calcium score. The internally developed model achieved a 32% cost reduction and reached a ROC-AUC of 0.75 using the VGG-19 architecture, demonstrating its ability to distinguish patients with different levels of coronary calcification—an important indicator of cardiovascular risk.
Tools used:
Python, TensorFlow, Scikit-learn, VGG-19, Pandas, NumPy, and Seaborn.
Jupyter Notebooks.
Google Cloud Storage and Google Compute Engine.
GitLab.
Development of a sales forecasting model for the Rossmann pharmaceutical retail chain using time series regression algorithms, aimed at cost reduction. The model reduced the average forecasting error from 36% to 4.65% (-31%) for the next six weeks, representing approximately €19 million in additional monthly revenue, using Kaggle data.
Tools used:
Python, Pandas, NumPy, Seaborn, Scikit-learn, SciPy, and Boruta.
Anaconda and Jupyter Notebooks.
Machine Learning with XGBoost Regressor.
Render Cloud.
Flask API.
Telegram Bot API.
Development of a customer segmentation model for a banking credit card portfolio using K-Means clustering, aimed at increasing profitability through targeted CRM strategies. The project grouped 8,950 clients into 8 behavioral clusters based on spending patterns, credit usage, cash advance behavior, and payment profile. As a result, the segmentation enables actionable strategies for revenue growth (upsell/cross-sell), cost reduction (more efficient campaigns), and risk mitigation (loss prevention). The expected annual ROI ranges from ~$30K (conservative) to ~$250K+ (optimistic), with a moderate expected impact around ~$100K/year, subject to A/B testing validation.
Tools used:
Python, Pandas, NumPy, and Scikit-learn.
Jupyter Notebooks.
Machine Learning with K-Means Clustering.
PCA for visualization and cluster interpretability.
Cluster evaluation metrics (Silhouette Score, Davies-Bouldin, Calinski-Harabasz)
Lead advanced Data Science and Machine Learning initiatives, leveraging Python, SQL, R and cutting-edge libraries to deliver predictive models, NLP solutions, and impactful analytics that drive business value and digital transformation. Collaborate closely with data engineering teams to ensure data quality, accessibility, and scalability, while presenting actionable insights to stakeholders.
Lead strategic initiatives in Data Science and AI by developing advanced metrics and innovative machine learning solutions that drive operational efficiency in healthcare and digital health. Collaborate with cross-functional teams to align projects with business goals and present impactful results. Core Responsibilities: Spearheaded the development and implementation of a Spine Orthopedics Dashboard using Generative AI (Gemini API), Python, Google Big Query, Airflow, Looker Studio and GCP. By providing real-time patient journey insights and automating key reporting processes, the dashboard enabled data-driven strategic decisions in Digital Health, resulting in a R$3.9 million revenue increase through optimized resource allocation and an 11% improvement in patient retention rates. Spearheaded the development of a Colorectal Cancer Dashboard using Generative AI, Python, Google Big Query, Airflow, and GCP, streamlining patient journey insights and extracting crucial information in Gap-in-Care for oncology screening, health management and generating savings in treatment costs for health insurance companies. Engineered a CNN-based model with VGG-19 architecture for Coronary Calcium Score Prediction with TensorFlow, Python, Google Compute Engine and GitLab, reducing imaging analysis costs by 32% while enhancing early cardiovascular risk detection. Implemented an automated Company Portal using QlikSense and BigQuery, generating 26 key operational indicators, eliminating manual validations, and contributing to an annual revenue retention of R$1.7M for Digital Health. Key Technologies and Tools: Python, R/RStudio, SQL, GCP, Databricks, Google Colab, Visual Studio, Google BigQuery VertexAI, Scikit-Learn, Pandas, Numpy, TensorFlow, Keras, GitLab, Airflow, QlikSense and Google Data Studio.
Owned the end-to-end design and development of executive dashboards, transforming complex healthcare datasets into intuitive, visually polished analytics products used by clinical and business stakeholders. Core Responsibilities: Identified and quantified cost-saving opportunities, uncovering potential annual savings of R$42M in the Safe Anticoagulant program, R$80M in Primary Care, and R$2M in Mental Health areas with innovation analytics projects. Led end-to-end analytics projects using MicroStrategy, R, Python, and VBA, streamlining health system performance monitoring and reporting. Developed dynamic dashboards and KPIs that provided actionable insights, driving strategic decision-making and enhancing operational efficiency across the organization. Key Technologies and Tools: Python, Pandas, Numpy, Excel, VBA, R/RStudio, MicroStrategy and Jupyter Notebooks.
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