Predictive Analytics & Retail Intelligence: Optimize Inventory, Pricing, and Demand

Turn historical data into a competitive advantage with precision-engineered Machine Learning models.

While GenAI handles text and images, Traditional ML handles the numbers that run your business. I help retail and supply chain companies move from "reactive" reporting to "proactive" forecasting. With 25 years of experience in ERP systems and localized Brazilian tax logic, I build models that account for the real-world complexities of the retail industry.

Core Offerings:

Demand Forecasting & Replenishment: Move beyond simple moving averages. I build XGBoost and Time-Series models (Prophet/ARIMA) that factor in seasonality, promotions, and regional trends to reduce stockouts by up to 30%.

Inventory Optimization (The "Virtual Stock" Solution): Using anomaly detection to identify discrepancies between your ERP and physical warehouse reality—reconciling accounting and commercial margins.

Dynamic Pricing & Markdown Optimization: ML models that suggest the optimal price point to maximize sell-through without sacrificing brand value.

Customer Churn & LTV Prediction: Identifying high-value customers at risk of leaving and triggering automated retention strategies.

Fraud Detection (Sefaz & Transactional): Anomaly detection for point-of-sale and tax-reporting data to flag irregularities before they become legal or financial liabilities.

Why Traditional ML? Unlike LLMs, these models are highly interpretable, cheaper to run, and provide the mathematical certainty needed for financial and supply chain decisions.

My Approach: I don't just "train a model." I build the Feature Engineering pipelines (GCP Dataflow/BigQuery) and ensure the MLOps (Vertex AI) is in place so your model stays accurate as market conditions change.