Shaiane
Tonin
Bridging Economics, Supply Chain experience, and Artificial Intelligence — building practical, value-driven ML solutions.
Background
ML Engineer with a background in Economics and supply chain operations. Currently pursuing a Master's in AI Engineering at INFNET, focused on building production-ready ML systems.
I studied Economics and Business at the Università di Torino and did an Erasmus exchange at EAE Business School in Barcelona. After graduating, I worked in Italy at a multinational spirits company — first as a supply chain logistics intern, then as an Order Fulfillment Planner, coordinating with planning, logistics, and warehouse teams using SAP.
That work gave me a concrete picture of how operational data flows — and where it doesn't. I transitioned to AI to apply that context to building ML systems: pipelines designed for real data quality issues, models built for production rather than benchmarks, and tools that fit into how teams actually operate.
Education
Master's in AI Engineering — INFNET
Background
Economics & Business — Università di Torino
Exchange
Erasmus+ at EAE Business School, Barcelona
Languages
Portuguese · Italian · English · Spanish
Perspective
Working inside a supply chain operation showed me where data breaks down in practice — inconsistent inputs, timing gaps, processes that don't match the system design. That experience shapes how I approach ML: I focus on what makes a system work in production, not just in a notebook.
Expert In Modern Stack
From data ingestion to production monitoring — full ML lifecycle
Python
- Scikit-learn
- TensorFlow
- Pandas
- NumPy
- Matplotlib
- Seaborn
- LightGBM
- Optuna
- NetworkX
Machine Learning
- Model Training & Evaluation
- Feature Engineering
- Hyperparameter Optimization
- Cross-validation
- SHAP Explainability
- Drift Detection
- Text Classification
NLP
- NLTK
- spaCy
- TF-IDF
- VADER Sentiment
- Named Entity Recognition
- Knowledge Graphs
- Text Preprocessing
- Regex Extraction
MLOps
- MLflow
- FastAPI
- Streamlit
- Data Quality Pipelines
- Model Monitoring
- CI/CD Automation
Data
- Data Preprocessing
- Exploratory Data Analysis
- Data Visualization
- SQL
- Power BI
Tools
- Git
- Jupyter Notebook
- SAP
- Microsoft Office
Languages
- Portuguese (Native)
- Italian (C2)
- English (C1)
- Spanish (C1)
Projects
Bank Customer Churn — MLOps
End-to-end production ML system for churn prediction
Production-ready MLOps implementation for bank customer churn prediction, developed as coursework for INFNET's MLOps discipline. Covers the complete ML lifecycle: data ingestion, quality validation, preprocessing, experimentation, dimensionality reduction, deployment, and post-deployment drift monitoring.
NLP Pipeline — Amazon Reviews
End-to-end NLP pipeline on 200k Amazon reviews across Beauty & Electronics domains
Complete Natural Language Processing pipeline applied to 200,000 Amazon customer reviews. Covers text cleaning, TF-IDF vectorization with bigrams, sentiment classification using VADER and supervised models, Named Entity Recognition with spaCy, and a knowledge graph built with NetworkX — comparing linguistic patterns across two contrasting product domains.
Education
Master's degree — Artificial Intelligence Engineering
INFNET
Rio de Janeiro, Brazil
Advanced studies in AI Engineering, Machine Learning and Deep Learning. Coursework includes end-to-end MLOps projects and production ML systems.
Erasmus+ Exchange Program
EAE Business School
Barcelona, Spain
Exchange semester focused on Statistics and Data Analysis, SEO, Digital Marketing, and Spanish language.
Bachelor's degree — Economics and Business
Università degli Studi di Torino
Turin, Italy
Undergraduate studies in Economics and Business, building a strong quantitative and analytical foundation that now underpins my approach to AI and data-driven decision making.