Data Scientist passionate about solving complex business problems through data-driven approaches. My work spans the entire data science lifecycle: from exploratory analysis and feature engineering to model development, deployment, and monitoring in production environments.

class DataScientist:
    def __init__(self):
        self.name = "Iñaki Rosello"
        self.role = "Data Scientist & CS Student"
        self.location = "Buenos Aires, Argentina"
        
    def expertise(self):
        return {
            "analytics": ["EDA", "Statistical Analysis", "Data Visualization"],
            "modeling": ["Classification", "Clustering", "Recommendation Systems"],
            "mlops": ["Model Deployment", "Monitoring", "CI/CD Pipelines"],
            "business": ["Fraud Detection", "Churn Prediction", "Demand Forecasting"]
        }
    
    def currently_learning(self):
        return ["Deep Learning", "Advanced MLOps", "Price Optimization"]

• 🎓 Computer Science - Universidad de Buenos Aires (Currently Studying)
• 📜 Data Science & MLOps Bootcamp - EDVAI (Completed)
• 🏆 AlixPartners Case Competition 2025 - Participant
• 💼 NoCountry Simulation - Data Science Team Member
• 📚 Continuous Learning: Deep Learning, Advanced Analytics (EDA, Data Preparation), Production ML

Current Learning Path:

• 🧠 Deep Learning fundamentals and advanced architectures
• 📈 Time series forecasting and demand prediction
• 🔧 Production-grade MLOps practices and automation
• 💰 Price optimization and econometric modeling
• 🐳 Scalable deployment with Docker and cloud services

Core Competencies:

• Analytics: Exploratory Analysis, Statistical Modeling, Business Intelligence
• Machine Learning: Classification, Clustering, Recommendation Systems, Fraud Detection
• MLOps: Model Deployment, Monitoring & Drift Detection, CI/CD Pipelines, Containerization
• Business Applications: Churn Prediction, Demand Forecasting, Price Optimization

Data Science Projects:

• ✅ Churn Prediction (FinTech) - Achieved 0.76 Recall through SMOTE + Logistic Regression optimization
• ✅ Fraud Detection System - Built production-ready fraud classifier with clustering + classification approach
• ✅ Recommendation Engine - Implemented hybrid RecSys with FAISS/ANNOY for sub-second retrieval
• ✅ Demand Forecasting - Created econometric optimization model for retail pricing strategy

Technical Skills:

• 📊 End-to-end data science workflows with imbalanced data handling
• 🤖 Model selection and hyperparameter optimization (RandomizedSearchCV, GridSearchCV)
• 🔍 Advanced feature engineering and SMOTE oversampling
• 📈 Model interpretability with SHAP and explainable AI
• 🚀 Production deployment with MLflow tracking and Evidently monitoring
• 📉 Drift detection and automated model retraining pipelines

Bootcamp & Competitions:

• 🎯 EDVAI Data Science & MLOps Bootcamp
• 🏆 AlixPartners 2025 Case Competition - Demand Forecasting Hackaton Competition
• 💼 NoCountry Job Simulation - FinTech Churn Prediction Project

# My typical approach to DS projects

def data_science_project(problem):
    """
    End-to-end data science methodology
    """
    # 1. Problem Understanding
    business_context = understand_business_problem(problem)
    success_metrics = define_kpis(business_context)
    
    # 2. Data Collection & Exploration
    data = collect_data(sources)
    insights = exploratory_analysis(data)
    
    # 3. Data Preparation
    clean_data = handle_missing_values(data)
    features = feature_engineering(clean_data)
    X_train, X_test = split_and_scale(features)
    
    # 4. Modeling
    models = train_multiple_models(X_train)
    best_model = optimize_hyperparameters(models)
    results = evaluate_model(best_model, X_test)
    
    # 5. Deployment & Monitoring
    api = deploy_model(best_model)
    monitor_performance(api)
    
    return production_ready_solution

I'm always open to collaborating on data science projects, discussing new techniques, or connecting with fellow data enthusiasts and professionals!