Data Scientist
Digital Mise en Place
Structure before scale. Order before automation.
Great data systems don't start with code—they start with clarity. My approach applies principles of operational thinking and data strategy to turn scattered processes into reliable, scalable solutions.
Philosophy
Every business process is a system. And every system, before being automated, must first be:
Understood → What's happening and why?
Mapped → Where does value flow—and where does it leak?
Prepared → What needs to be standardized or cleaned?
Rebuilt → How can it be made reproducible, adaptive and low-friction?
Like a kitchen preparing for service, I focus on mise en place: organizing inputs, designing clean flows, and building systems that run themselves—with minimal intervention.
🔧 My Role in That System
I don't just automate—I architect:
Detect bottlenecks through data audits
Model key flows using graph logic and modular thinking
Build scalable tools: from backend scripts to workflow engines
Ensure traceability, maintainability, and business alignment
I don't deliver dashboards—I deliver decisions made easier.
I don't automate for the sake of it—I create clarity at scale.
Where I generate the most impact with AI and automation
These are the areas where I apply my capabilities to solve real bottlenecks, reduce operational costs and accelerate decisions with data.
Operational Efficiency
I design solutions that standardize processes, reduce operational variability and improve coordination between teams using reproducible and automated flows.
Analysis and Decision Making
I transform raw data into actionable insights. I build analytical models, pipelines and dashboards so decisions are made with precision and in real time.
Strategic Automation
I automate internal flows (APIs, ETL, reporting, event triggers) with low-code solutions and pure code, prioritizing maintainability and performance.
Projects that Speak for Themselves
Success cases that demonstrate the real impact of AI solutions in business
Restaurant Revenue Prediction System
Regression model that predicts restaurant revenue based on demographic, real estate and commercial data. TFI dataset with 137 restaurants in training and 100,000 in test.
Chatbot for Reservation Management
Hybrid system that combines local intent detection with OpenAI to manage reservations 24/7. Automatically detects queries about menu, hours, availability and schedules appointments without human intervention.
Research Portfolio
Machine Learning, Computer Vision and advanced data analysis projects
Tech Stack
Technologies learned through certified courses
Data Science & Python
Career: Data Scientist with Python
Python
Completed Courses:
Pandas & NumPy
Completed Courses:
Matplotlib & Seaborn
Completed Courses:
Machine Learning & EDA
Completed Courses:
Data Analysis & BI
Career: Foundations for Analytics and Data Science
SQL
Completed Courses:
Business Intelligence
Completed Courses:
Automation & AI
Career: Work Environment for Data & AI
N8N
Completed Courses:
AI Agents
Completed Courses:
Bubble (No-code)
Completed Courses:
🚀 Evolution Roadmap
Foundations
Consolidation of knowledge in Python, SQL and BI, essential for any Data Scientist.
AI & Automation Specialization
Application of Data Science skills to build tangible business solutions, focused on automation and artificial intelligence, using tools such as AI agents, n8n and generative models.
Next Level: AI Agents Architect
Transition from Data Scientist to expert in AI agent architecture. Deepen knowledge in building and orchestrating AI agents (RAG, MCP), master vector databases, develop multi-agent systems and build generative AI solutions applied to specific business niches.
Learnings and Automation
Actionable ideas, no empty promises. Real stories of automation, efficiency and technical decisions that actually worked.
Recibí insights semanales sobre automatización y eficiencia.