// Curriculum Vitae

Juan Miguel Gutierrez

Machine Learning Engineer and Data Scientist with 4+ years across industry and research, deploying systems spanning reinforcement learning, time series, computer vision, generative AI, NLP, and LLM-powered applications. Completing an MSc in Mathematical Engineering (Statistical Learning) at Politecnico di Milano on a government scholarship (Top 5%).

2025–now
IMHTP — AI Engineer / Data Scientist · Milan, Italy
Design multi-agent LLM pipelines (SOTA foundation models + NLP pattern-matching) for automated healthcare data processing, reaching error-free extraction across all manually validated cases. Prototyped STT/TTS models and a real-time computer-vision anomaly-detection pipeline (DRAEM + temporal deep learning, 91% accuracy) for industrial process evaluation. Earlier, benchmarked survival-analysis models (Cox PH, Kaplan–Meier, XGBoost Survival, DeepSurv), reaching a 0.70 concordance index on clinical data.
2022–2024
Mercado Libre — Semi-Senior AI / Data Science Engineer · Bogotá, Colombia
Improved median stock health by ~15% and reduced overstock by 10% via probabilistic forecasting integrated into inventory optimization. Pioneered Reinforcement Learning and Contextual Bandits for dynamic pricing and order-quantity models. Built LLM agents with multi-agentic workflows and advanced prompting (Tree-of-Thought) for executive decision support. Contributed to a Stable Diffusion image-generation initiative and a Tableau seller-funnel dashboard.
2022–2024
TripleTen LatAm — Data Science Code Reviewer · Remote
Reviewed and validated 200+ student projects across Python, SQL, statistics, supervised/unsupervised ML, time series, NLP, and computer vision — mentoring learners on accuracy and best practices.
2021–2022
Quantil — Data Scientist (Researcher) · Bogotá, Colombia
Built a record-matching algorithm achieving 80% match rate on partial data. Implemented gas-shortage estimation methods that reduced client costs by 25%. Worked across time series, supervised learning, and dynamic panel-data models using Python, R, and STATA.
2020–2021
Universidad del Rosario — Research Assistant · Bogotá, Colombia
Researched differential-privacy algorithms at the Tic Tank research group, contributing to a published workshop paper at ICML LatinX in AI (LXAI) 2022.
2024–27
Politecnico di Milano — MSc Mathematical Engineering
Statistical Learning Track. Invest Your Talent in Italy Scholarship (Top 5%) & Colfuturo Scholarship Loan. Coursework: AI & Computer Vision, High-Performance (CUDA) Computing, Graph ML, Streaming Data Engineering, Statistical Signal Processing, Online Machine Learning.
2019–21
Universidad del Rosario — BSc Applied Maths & Computer Science
Minor: Artificial Intelligence. Coursework: NLP, Machine Learning, Computer Vision, Optimization, Data Structures & Algorithms.
2016–20
Universidad del Rosario — BA Economics
Coursework: Econometrics, Time Series, Statistics. School Alliance Scholarship (top 3 of applicant pool).
Python R C++ / CUDA SQL · BigQuery PyTorch JAX TensorFlow Scikit-learn · XGBoost Stable Baselines3 LangChain HuggingFace Docker AWS · GCP Spark React / TypeScript Tableau
Deep RL (Stable Baselines3, Gymnasium) — Hugging Face, 2023 Unsupervised Learning, Recommenders, RL — DeepLearning.AI, 2022 Accelerated Computing in CUDA C/C++ — NVIDIA, 2025

A Model-Based Filter to Improve Local Differential Privacy

Gutierrez, J. M. et al. · ICML 2022 · LatinX in AI Research Workshop · Baltimore, MD

→ doi.org/10.52591/lxai202207171
Spanish (Native) · English (C1, IELTS 7) · Italian (Limited Working) · French (Elementary)