Analytics Engineer | Computational Physicist |
Healthcare Data Specialist
PhD in Astronomy → building data pipelines that actually
ship to production.
- 🌍 Brazilian living in Colombia, eligible to work in LATAM and the EU (Italian passport)
- 🔭 Former astrophysicist: 15+ years of quantitative modeling, Bayesian inference, and large-scale data analysis
- 🏥 Currently building MRI scheduling optimization pipelines at XRDOSE, deployed at Colombian hospitals
- 🎯 Transitioning from academia to industry with a focus on Analytics Engineering and Data Engineering
- 🌐 Fluent in Portuguese, English, and Spanish
Languages & Querying
  
Cloud & Infrastructure
  
Data & Analytics
  
Coming soon: dbt · Apache Airflow · PySpark
[XRDOSE — deployed at Clínica Imbanaco, Cali, Colombia]
ETL pipeline and analytics dashboard for hospital resource management
- Problem: MRI machines in Colombian hospitals have significant scheduling inefficiencies, with idle time difficult to quantify or act on
- Solution: Built end-to-end data pipeline ingesting raw clinical logs, transforming scheduling data, and delivering interactive heatmap visualizations
- Technologies: C#, ASP.NET Core, Azure SQL, ECharts, Git
- Impact: Production-ready tool deployed at Clínica Imbanaco and Clínica Sommer Medellín, enabling data-driven scheduling decisions for operations teams
- Key feature: "Opportunity Counter" — calculates recoverable study capacity from historical idle time
[SENA ISO 17025 Laboratory, Medellín 2024]
Python automation for metrology and statistical quality control
- Problem: Manual validation of calibration measurements was time-consuming and error-prone
- Solution: Automated pipeline for measurement validation, uncertainty quantification, and statistical reporting
- Technologies: Python (pandas, numpy, matplotlib), Monte Carlo simulation, Bayesian inference, ANOVA, R&R studies
- Impact: Significant reduction in manual processing time; improved audit readiness for ISO 17025 accreditation
[Universidade de São Paulo, 2011–2017]
Large-scale statistical modeling of stellar evolution
- Problem: Modeling Be star disk evolution required processing decades of multi-wavelength observational data with significant noise and uncertainty
- Solution: Custom Python pipelines using MCMC (Markov Chain Monte Carlo) Bayesian inference for parameter estimation across massive datasets
- Technologies: Python, Fortran, HPC cluster computing, statistical inference, scientific visualization
- Impact: 5 peer-reviewed publications in international astronomy journals; methodology transferable to any large-scale data modeling problem
- dbt (data build tool) — Analytics Engineering core
- Apache Airflow — pipeline orchestration
- Python OOP — production-grade code structure
- Star Schema & Data Warehouse design
- LinkedIn: linkedin.com/in/lrrimulo
- Email: [email protected]
- Location: Colombia (remote-first)
"The skills that let you model a collapsing stellar disk or extract signal from noisy telescope data are the same skills that build reliable data pipelines. Physics taught me to think in systems. Industry taught me to ship them."