Christian Quidel
Data Scientist Trainee • MLOps • Python & Cloud Architecture
Oracle Next Education (ONE) & Alura Latam graduate with a prior background in high-stakes diagnostic imaging (ICU, Coronary Care, Neonatology). I transform complex data into high-performance, production-ready Machine Learning pipelines, specializing in sub-millisecond ONNX inference, cloud infrastructure (OCI), and antifragile statistical rigor.
Featured Machine Learning Projects
End-to-end data science solutions: NLP, inference latency optimization, and containerized cloud deployment.
FinanceAI – Intelligent Financial Diagnostic & NLP Platform
End-to-end Machine Learning platform engineered during the Oracle Next Education (ONE) Hackathon. Features an NLP transaction classification pipeline, a multivariate financial health diagnostic model, and multi-container cloud deployment.
Key Technical Highlights:
- • Categorized 240,000+ financial records across 10 classes using Scikit-learn (TF-IDF + Linear Classifier) evaluated via Macro & Weighted F1-Scores.
- • Serialized Scikit-learn pipelines to ONNX (skl2onnx) for native in-memory execution inside a Java 17 / Spring Boot backend (< 0.8ms latency).
- • Enforced strict data leakage prevention via temporal splits and optimized hyperparameters using GridSearchCV across 36 candidates (Stratified CV).
- • Orchestrated 4 microservices (Vue 3, Spring Boot, MySQL, Jupyter) using Docker Compose and deployed to Oracle Cloud Infrastructure (OCI Compute).
Predictive Modeling & Tabular Data Analytics Engine
Modular experimentation environment for large tabular datasets. Implements IQR outlier filtering, continuous feature transformation, and rigorous trade-off analysis across classification and regression algorithms.
Key Technical Highlights:
- • Multidimensional exploratory data analysis and statistical visualization using Pandas, Matplotlib, and Seaborn.
- • Automated preprocessing pipelines: missing value imputation, StandardScaler normalization, and One-Hot Encoding.
- • Evaluated baseline models (Logistic Regression, Decision Trees) using ROC-AUC curves, Confusion Matrices, and weighted F1-Scores.
- • Clean, reproducible technical documentation adhering to PEP 8 code standards.
Inference Benchmark & Model Validation
Empirical comparison of inference latency between pure Python microservices and native ONNX serialization in Java 17 (Spring Boot).
Inference Latency per Request (ms)
↓ 98.2% ReductionProduction Results
Inter-service HTTP overhead + Python runtime.
Direct in-memory execution without network hops.
Posts & Threads on X
Notes on Machine Learning, MLOps, statistical validation, and cloud architectures.
Upcoming Technical Publications
I will be sharing practical case studies on Machine Learning pipelines, MLOps, and data engineering on my official X account.
From Clinical Radiology to Data Science
My healthcare background in diagnostic imaging within high-pressure hospital units (ICU, Coronary Care, Neonatology) instilled an uncompromising standard: absolute precision with noisy data and calm execution under pressure.
In radiology, I learned to distinguish signal from noise before making high-stakes decisions. Transitioning to Data Science and Machine Learning was a natural evolution: transforming unstructured data into resilient, reproducible predictive systems.
I strongly embrace critical thinking and Nassim Nicholas Taleb's principles of antifragility: before trusting an accuracy metric, I evaluate out-of-distribution risks, heavy-tailed uncertainty, and enforce strict data leakage prevention.
- Interpretable models with rigorous cross-validation.
- MLOps and containerized cloud deployment (Docker / OCI).
- Clear API data contracts and cross-functional communication with Software teams.
Active Courses & Technical Upskilling
Currently expanding my engineering toolkit with direct focus on cybersecurity, containerization, and production infrastructure.
Cybersecurity
Foundational security engineering and threat modeling: network defense protocols, security information and event management (SIEM), incident triage, and Python scripting for automated security operations and data integrity protection.
Curso Profesional de Docker
Hands-on containerization of Machine Learning pipelines and microservices: optimized multi-stage Dockerfile architecture, multi-container Docker Compose orchestration, volume persistence, and isolated network configuration.
Looking to hire a Data Scientist Trainee with a production mindset?
I am available to join global teams in 100% remote roles. Open to Data Science, Machine Learning, or Data Analytics positions.