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Proactive senior data scientist with vast experience on image recognition/segmentation, natural language processing tasks, and physical quantification of real-world data . Expert in cloud computing (AWS/GCP), Python, and relevant ML and computer vision frameworks, such as TensorFlow and PyTorch. I am also knowledgeable in algorithm parallelization (with and without GPUs). Very independent and communicative.

Python | Scikit-learn | PyTorch | Pandas | NumPy | NetworkX | Git | LaTeX I am a Physics Ph.D. student at FaMAF, UNC. My thesis is about robustness and community detection on complex networks. I also teach physics as an assistant professor at UNC and UNLPam. My main research interest is the theory of Complex Networks and Systems from a statistical physics approach. I am also a data enthusiast and work independently as a data scientist.

I help companies build machine learning systems, automate workflows, and turn complex data into reliable decision tools. Over the past 7+ years I’ve worked across machine learning, LLM workflows, data pipelines, and Excel VBA automation, building systems that connect models, databases, APIs, and business tools into practical solutions that run in real environments. Many companies have valuable data but lack the infrastructure or automation to fully use it. My role is usually to design and implement systems that transform messy processes into reliable, scalable workflows. Clients typically hire me to: • Build or improve machine learning models • Develop LLM-powered workflows and AI automation • Design scalable data pipelines for analytics and ML • Clean, structure, and analyze complex datasets • Automate operational workflows and reporting • Build advanced Excel VBA automation tools Machine Learning & AI I build predictive models that help businesses make better decisions from their data and automate parts of decision-making processes where appropriate. Depending on the use case, these models can support human decisions or operate as part of automated workflows that detect patterns, generate predictions, and trigger actions. My goal is always to ensure these systems are reliable, interpretable, and safe to use in real operational environments. My focus is always on models that deliver measurable value and can run reliably in production environments. LLM Workflows & AI Automation I design systems that use large language models to automate complex tasks involving text, documents, or decision processes. Examples include automated classification systems, information extraction pipelines, document processing workflows, and AI agents that interact with APIs and databases. The goal is always to turn LLMs into structured, reliable workflows rather than isolated prompts. Data Pipelines & Data Engineering Successful AI systems depend on solid data infrastructure. I design and implement pipelines that ingest data from APIs, databases, and external sources, clean and transform it, and make it ready for analytics or machine learning. This includes ETL pipelines, analytics datasets, and systems designed to handle growing volumes of data efficiently. Excel VBA Automation Many organizations still rely heavily on Excel for operational processes. I build advanced VBA automation tools that eliminate repetitive work, streamline reporting workflows, and connect Excel with databases or APIs. These solutions are often used by operations, finance, and analytics teams to replace manual processes and significantly reduce time spent on data-heavy tasks. Example projects I’ve built include: • Machine learning models predicting customer churn and business KPIs • LLM workflows that classify and process thousands of documents automatically • Data pipelines ingesting API data into analytics databases for reporting and modeling • Automation systems replacing manual reporting workflows • Excel VBA tools used to automate complex reporting and operational processes I focus on solutions that are clean, reliable, and easy to maintain. Most of my projects involve connecting advanced AI systems with real business infrastructure — databases, APIs, dashboards, Excel tools, and automated workflows. In most cases these systems translate directly into measurable impact: reducing hours of manual work, automating operational decisions, improving forecasting accuracy, or enabling teams to act on data that previously wasn’t usable. The goal is always to turn data and automation into tangible business value: saving time, improving efficiency, and supporting better decisions at scale. Tech stack I commonly work with: Python, SQL, BigQuery, PostgreSQL, Pandas, Polars, machine learning frameworks, LLM APIs, ETL pipelines, and Excel VBA. If you're looking for someone who can bridge machine learning, data engineering, automation, and Excel-based workflows, I’d be happy to discuss your project.

Passionate about building scalable and efficient backend systems, I specialize in Python development with a strong focus on building machine learning models, data scraping, RESTful APIs, and infrastructure. With hands-on experience in SQL databases and cloud services, I love tackling complex problems and optimizing workflows. 🔹 Key Expertise: * Machine learning pipelines (AWS' Sagemaker, Keras, Tensorflow) * Web Scraping & Automation (Selenium, Scrapy, Beautiful Soup) * RESTful API Development (Flask, SQLAlchemy) * Infrastructure & Deployment (AWS, Docker) * Database Management (MySQL, PostgreSQL) Previously, I worked at Upwork as a Senior Machine Learning Engineer in the fraud department, where I contributed to fraud detection systems, data pipelines, and automation. I’m always open to collaborating on exciting new projects—let’s build something great together!

I am an Artificial Intelligence Consultant and Machine Learning Engineer with more than 8 years of experience developing scalable, data-driven solutions for companies in technology, telecommunications, fintech, and e-commerce. My focus is on designing intelligent automation systems, conversational agents, and predictive models that improve operational efficiency and decision-making. I have collaborated with global clients such as Google, Deloitte, Globant, and Telecom, delivering high-impact AI implementations using LLMs (Gemini, GPT, LlamaIndex), LangGraph, Vertex AI, AWS SageMaker, and Google Cloud Platform. As CTO and Co-Founder at Nexia Capital, I lead the development of custom AI agents and microservices for businesses seeking to integrate automation with intelligence — bridging the gap between advanced models and real business results. Core competencies: AI Chatbots and Conversational Systems (Dialogflow CX, LangGraph, LlamaIndex) Process Automation & Cloud Microservices (AWS, GCP, Vertex AI, CI/CD) Predictive Analytics and Data Engineering (Python, SQL, Pandas, scikit-learn) Computer Vision & NLP (YOLOv8, LLMs, Prompt Engineering) Machine Learning Architecture & Deployment (Docker, API, MLOps) I’m passionate about transforming complex workflows into intelligent, automated systems that drive measurable ROI.

As a seasoned data science engineer with over 4 years of experience, I bring a comprehensive skill set to the table. Biomedical Engineer turned Data Scientist with experience in Python-based Data Analysis, Machine Learning, and LLM training & evaluation using RLHF methodologies. Skilled in designing and executing ML pipelines, data annotation, ETL workflows, and model evaluation. Experienced with large-scale datasets, APIs, and structured data in regulated environments. Throughout my career, I have successfully delivered a wide range of projects, from small-scale web applications to large enterprise systems, always focusing on delivering high-quality solutions that meet both technical and business requirements. With a passion for learning and staying up-to-date with the latest technologies and best practices, I am committed to continuously enhancing my skills and driving innovation in every project I undertake. Currently exploring remote opportunities to contribute to international teams, bringing strong problem-solving, proactivity, and collaborative skills. Technical Skills: Python (Pandas, NumPy, SciPy, Scikit-learn), ML frameworks (TensorFlow, PyTorch, Keras), Data Visualization (Matplotlib, Plotly, Seaborn), LLM evaluation & RLHF, Computer Vision (OpenCV, YOLO, Mediapipe), SQL & NoSQL, FastAPI/Django/Flask.