j
jadee_chen

Jade C.

@jadee_chen

Senior AI Engineer, Tech Lead: Optimization, AI Engineering, ML, DL Pipelines

États-Unis
Anglais, Chinois
Certaines informations sont présentées en anglais.
À propos de moi
Senior Data Scientist & Tech Lead (6+ yrs) with an MS in Applied Math from UC Santa Cruz. I build production-grade AI systems, local LLM infrastructure, and supply chain models. SPECIALTIES: • Local LLM Ops: Private vLLM/Ollama setup (DeepSeek/Qwen), AI agents & RAG • Operations Research: Supply chain simulation (SimPy/Monte Carlo) & MILP • Production Apps: FastAPI REST APIs, Streamlit dashboards & Docker I turn complex requirements into clean, low-latency, scalable code. Message me to start!... Plus d’infos

Compétences

j
jadee_chen
Jade C.
hors ligne • 

Voir mes services

Tableaux de bord des données
I will build interactive streamlit dashboards and production fastapi ml backends
De100 $US / projetPlus de détails
Modélisation et analyse statistique
I will do statistical analysis and python data science for business insights
De50 $US / projetPlus de détails

Portfolio

Expérience professionnelle

Mindray

Senior Data Scientist & Tech Lead

Mindray • Temps plein

May 2024 - Jun 2026 • 2 yrs 1 mo

• Established and grew the first Data Science & AI team for the IVD Supply Chain department from the ground up, introducing Agile workflows and technical roadmaps. • Architected production ML pipelines bridging legacy ERP systems with modern forecasting models to optimize enterprise-wide supply chain logistics. • Developed Streamlit-based monitoring interfaces for real-time model validation, tracking inventory drift and loss calculations for production reliability. • Mentored 2 fresh-graduate junior data scientists in machine learning modeling, code quality standards, and distribution center logistics optimization.

Medidata

Senior Data Scientist

Medidata • Temps plein

Oct 2021 - Apr 2024 • 2 yrs 6 mos

• Led a cross-functional team to deploy Anomaly Detection models (Isolation Forest, LOF), replacing legacy statistical methods for clinical data quality control. • Optimized XGBoost/LightGBM enrollment models, integrating longitudinal data pipelines for clinical trial forecasting. • Deployed validation dashboards to track model variance, LLM hallucinations, and behavior drift in production environments.