EXECUTIVE PROFILE
Principal Machine Learning Scientist with 8+ years developing predictive recommendation engines, LLM fine-tuning pipelines, and high-frequency anomaly detection models for Big Tech. Holds 4 AI patents in deep reinforcement learning.
PROFESSIONAL EXPERIENCE
Apex Neural Technologies
- Designed transformer-based personalization model increasing click-through-rate by 28% and driving $42M incremental GMV.
- Architected distributed inference infrastructure handling 350M daily API predictions with sub-15ms p99 latency.
- Authored continuous automated model drift retraining pipelines using MLflow and Kubeflow.
Veritas Analytics Lab
- Built fraud detection classifier scoring 12M credit card transactions daily with 99.8% precision.
- Mentored 8 junior data scientists and published 5 peer-reviewed papers at NeurIPS and ICML.
CORE COMPETENCIES & SKILLS
PyTorch
Deep Learning & Transformers
Distributed Training (Ray/vLLM)
MLOps & MLflow
Python
C++
Docker/Kubernetes
Vector DBs (Pinecone/Milvus)
EDUCATION & CREDENTIALS
Ph.D. in Computer Science & Artificial Intelligence
Stanford University
2017
B.S. in Applied Mathematics & Statistics
Carnegie Mellon University
2013