About Jorge
I help data-driven companies turn ML models into reliable, production-grade systems that deliver measurable business outcomes, not just notebooks that sit on a shelf.
6+ years as a Data Scientist and ML Engineer, with a 5-year Software Engineering foundation that means I ship end-to-end: from exploratory analysis and model development through containerized deployment, monitoring, and SLO-driven data quality.
What I typically deliver: customer churn and lifetime value models (LightGBM), automated batch prediction pipelines on GCP Vertex AI, multi-container Docker/Kubernetes MLOps workflows, A/B testing frameworks with causal inference, and anomaly detection systems for energy and retail analytics.
What sets me apart: I've owned the full ML lifecycle at enterprise scale (Walmart), optimizing loyalty program budgets through CLV models while maintaining 99.9% data quality SLOs across production dashboards. I don't hand off to engineering; I am the engineer.
Stack: Python, PyTorch, LightGBM, scikit-learn, Docker, Kubernetes, GCP (Vertex AI, BigQuery), AWS, Terraform, FastAPI, Spark, dbt.
Spanish
Native or bilingual
German
Basic
English
Fluent
Can work on-site
Hamburg (up to 10km)
Experience
- LeprconSenior Data ScientisCONSULTING AND AUDITSNovember 2024 - April 2025 (5 months)Hamburg, Germany
- Architected data integrity pipelines for energy analytics
- Detected anomalies through statistical validation with Pandas and Statsmodels
- Applied hypothesis testing to mitigate technical and business risks with scikit-learn
- Translated statistical findings into actionable business recommendations
- Walmart ChileSenior Data ScientistRETAIL (LARGE RETAILERS)January 2022 - February 2024 (2 years and 1 month)Valparaíso, Chile
- Implemented MLOps strategies for customer analytics
- Automated multi-container Docker/Kubernetes pipelines on Vertex AI (GCP)
- Audited and operationalized machine learning models
- Developed monthly batch predictions for Customer Lifetime Value (CLV) models using LightGBM
- Optimized loyalty programs budget by using CLV modelsd
- Maintained 99.9% data quality SLOs for sales dashboards via automated BigQuery SQL scripts and Cloud Monitoring
- Walmart ChileData ScientistRETAIL (LARGE RETAILERS)July 2020 - December 2021 (1 year and 5 months)Valparaíso, Chile
- Designed and deployed production-grade LightGBM Churn Model using feature engineering (SQL-derived attributes and business segment tags) to predict and mitigate customer attrition.
- Engineered unsupervised learning models using K-means and DBSCAN for customer segmentation
- Conducted A/B testing with Statsmodels and SciPy and diff-in-diff to drive increases in marketing ROI
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Education
- Post-graduate courseworkUniversidad Técnica Federico Santa María (UTFSM)2019M.Sc. in Informatics Engineering
- B.Sc. in Informatics EngineeringUniversidad Técnica Federico Santa María (UTFSM)2014Informatics Engineering (Computer Science)