Vetted Data Visualization Professionals

Kamilah Patterson

Screened ReferencesModerate rec.

Senior HR Business Partner specializing in talent strategy and workforce analytics

Brooklyn, New York11y exp

“HR Business Partner with GroupM experience supporting multiple mergers, building and transitioning Analytics/Programmatic/Account Management teams through hands-on job architecture, benchmarking, and performance management. Uses HR analytics (including an Excel-based retention model with visual reporting) and cross-functional partnership with IT to reduce disruption and improve retention during restructuring and major policy changes like return-to-office.”

“Robotics software engineer who built a ROS Noetic-based perception-to-control stack for a pick-and-place robotic arm, integrating OpenCV/TensorFlow vision with motion planning and PID tuning. Demonstrated strong real-time debugging skills (rosbag, queue/latency fixes) and experience deploying reproducible robotics environments with Gazebo simulation, Docker, and GitLab CI.”

“Frontend engineer with hands-on experience leading architecture and quality practices for React/Angular apps, including design system selection, code review/branching workflows, and Jest-based unit testing with a 100% coverage target. Built a React + TypeScript financial tool using Zustand/React-Redux, improved performance via lazy loading, and implemented input-sanitization utilities. Has managed fast-paced releases with Rally-based defect tracking and resolved a production deployment issue via rollback and YAML configuration fixes.”

“ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.”

“Built a production industrial anomaly detection system for a laminator using only limited runtime logs (time/pressure/temperature) and scarce abnormal examples. Addressed inconsistent manual labeling across customers by creating an operator feedback loop for remarking predictions and retraining customized models, and communicated results to a non-technical company liaison using clear tables, trend plots, and interactive demos.”

“AI/ML Engineer who built a production RAG-based LLM system for insurance policy documents, turning thousands of messy PDFs into a searchable index using LangChain, Azure AI Search vectors, hybrid retrieval, and FastAPI. Strong focus on evaluation (MRR/precision@k/recall@k, REGAS) and performance optimization (vLLM), with prior clinical NLP experience using BERT-based NER validated on ground-truth datasets.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”