Personal details
Oscar M.
Summary
Senior and Lead Data Scientist (+15) --- Data Engineer --- Power BI Developer --- Data Consultant --- Machine Learning & Deep Learning --- Forecasting
Technical skills: Data Scientist, Data Engineering, Data Analyst, Power BI Developer, Programming Languages (Python, R, SQL, etc.), Cloud (Azure + GCP), Time Series, Data Visualization, Statistics, Econometrics, Trainer and Consultant.
Soft skills: Communication, Adaptability, Teamwork, Problem-solving, Innovation, Time management, Critical thinking, Initiative, Decision-making, Leadership, Organizational, and Creativity.
Languages: English, French and Spanish. Basic level in Portuguese.
Data Analysis : Machine Learning (ML) & Deep Learning (DL), Time Series, Visualization, A/B Testting, Simulation
AI: LLM, Generative, Deep Learning, NLP, Fine Tuning, Image recognition, Chatbot
Cloud: Azure Databricks, AI Studio, Blobs,... AWS SageMaker, Athena, Glue,... GCP Big Querry,
Programming: Python, SQL, R, JavaScript, SAS, PySpark, Git, DAX, Scala, Bash, Markdown
Work Experience
Energy Demand Forecasting: DL & ML models on electricity demand in days, hoursand 15min (LSTM, MLP, CNN and ML Regression techniques in AI & ML).
Dashboard & Reports: Design and implement reports, dashboards, and analysis(Power BI).
Demand Energy Data Structure: ELT processes and analysis using (Python, PySpark &Databricks).
Power Grid Management: DL & ML to improve the durability and balance of powergrids (Python, AI - ML & PBI).
Financial Transaction Tracking System: ML platform to track and analyze financialtransactions in real time, allowing immediate detection of suspicious activities(Python, Databricks & PBI).
Massive Forecasting: DL & ML development in revenues and expenditures using areact Analytic procedure (R & Shiny).
Early Warning System against Corruption: ML system to detect and alert onpossible cases of corruption, using public and private data (Python, Databricks &PBI).
Financial Fraud Detection: Neural networks analysis to identify complex and subtlepatterns in financial transactions that may indicate fraudulent activities (Python, R,Looker).
Databases for Financial Analysis: Design and maintenance databases (collect andorganize financial data), facilitating its analysis and improving financial datastructure (Python, SQL Server & MySQL).
Financial Verification Process: Automated system for the verification of financialtransactions, minimizing the time required to detect suspicious activities (Python).
Fraud Detection in public procurement: Development of a solution that allows toidentify fraudulent financial statements, using supervised and unsupervisedalgorithms (Python, Databricks & PBI).
Anomaly Detection in Financial Fraud: Implementation of supervised, semi-supervised and unsupervised learning techniques to detect fraudulent activitiesafter their commission, thus saving potential costs for the company (Python, R &Looker).
National surveys of citizen perception: Developed, implemented, analyzed anddisseminated the perception of citizens on issues of financial interest (GoogleWorkspace, Looker & Tableau).