📈 Senior FP&A & Finance Analytics | Financial Modeling | Forecasting | Data-driven Finance | Python | SQL | Power BI
Senior FP&A and Finance Analytics professional with 15+ years of experience in financial modeling, reporting, forecasting and finance transformation.
I design data-driven finance solutions by translating finance requirements into scalable analytical workflows using Python, SQL, Excel and BI tools.
My work sits at the intersection of Finance, Data and Analytics, building integrated FP&A environments that combine financial modeling, data pipelines, statistical forecasting and executive dashboards.
Recent work includes the design of an end-to-end Finance Analytics Platform simulating a modern finance data stack:
Financial Modeling → Data Warehouse → Python Automation → Forecasting → BI Dashboards.
📌 Financial Modeling & FP&A
Valuation (DCF, Multiples), integrated financial statements (P&L, Balance Sheet, Cash Flow), forecasting, budgeting, scenario analysis, KPI frameworks, capital allocation.
📌 Finance Data & Analytics
Financial data pipelines, data warehousing, IFRS reporting structures, financial forecasting models, scenario simulation, variance analysis.
📌 Programming & Data Engineering
Python (Pandas, NumPy, SQLAlchemy, statsmodels), R, SQL, Excel (advanced), Power Query, ETL/ELT pipelines.
📌 Data Science & Forecasting
Statistics, time-series modeling, SARIMA models, machine learning applications in finance, predictive analytics.
📌 Business Intelligence & Visualization
Power BI, Tableau, dashboard design, financial performance monitoring, executive reporting.
📊 Digital Finance Forecasting & Analytics Platform
End-to-end Finance Data Stack that integrates financial modeling, data engineering, statistical forecasting, and business intelligence into a unified analytics platform.
Key components:
• Integrated IFRS 3-Statement financial model (Excel)
• Financial Data Warehouse (PostgreSQL Star Schema)
• Python automation pipeline for financial reporting
• Statistical forecasting models (SARIMA) and scenario simulation
• Power BI executive dashboards
The project demonstrates how modern finance teams can move from spreadsheet-based reporting to scalable analytics-driven FP&A systems.
📈 Volatility Forecasting with HAR-Type Models
Master’s thesis in Finance (University of Minho) investigating financial market volatility forecasting using HAR-type econometric models implemented in R.
The research evaluates 16 variations of HAR-based models applied to FTSE-100 realized volatility, incorporating jumps, signed jumps, realized semivariance, and leverage effects, with model performance assessed through multiple loss functions and statistical validation techniques.
Predicting corporate financial distress using machine learning and financial data analysis. Implements Random Forest, XGBoost, SVM, and more to classify bankrupt vs. non-bankrupt companies.
🏦Bank Customer Churn Prediction
Predicting customer churn in the banking sector using machine learning and customer data analysis. Models compared include Random Forest, XGBoost, and SVM, with an emphasis on optimizing recall over accuracy for better retention strategies.
📡Telecom Customer Churn Prediction
Predicting customer churn in the telecom industry using machine learning and feature selection techniques. Implements Random Forest, XGBoost, and CatBoost, with data balancing (SMOTE, undersampling) and hyperparameter tuning to maximize recall and identify at-risk customers.
A web application for startup acceleration and mentorship management.
📩 Email: gustavo.provento@gmail.com
💼 LinkedIn: linkedin.com/in/gustavomfreitas
📂 GitHub: github.com/gustavomfreitas
🌍 Provento Gestor: gustmf.pythonanywhere.com