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Alexander Conn
Principal Data Scientist specializing in cybersecurity ML and MLOps
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About
ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).
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Key Strengths
- Designed end-to-end ML/NLP architecture and pipeline for unstructured internal documents
- Improved information retrieval/search time by 30% for security teams via semantic search
- Strong model validation rigor (cross-validation; Precision@k, MRR; precision/recall/F1; Silhouette, ARI)
- Hands-on fine-tuning of BERT with domain-specific data (security logs, technical manuals) including custom tokenization for jargon
- Built scalable, production-grade Python workflows using Spark/Dask/Docker with testing, monitoring, and logging
- Scaled real-time anomaly detection processing millions of events/day using Spark and AWS Lambda
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