EXECUTIVE SUMMARY
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
Principal ML Engineer & Team Lead
2021 – Present
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.
Senior Machine Learning Scientist
2017 – 2021
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.
EDUCATION
Ph.D. in Computer Science & Artificial Intelligence
Stanford University
2017
B.S. in Applied Mathematics & Statistics
Carnegie Mellon University
2013
AREAS OF EXPERTISE
- PyTorch
- Deep Learning & Transformers
- Distributed Training (Ray/vLLM)
- MLOps & MLflow
- Python
- C++
- Docker/Kubernetes
- Vector DBs (Pinecone/Milvus)