Candidate Information Name Available: Register for Free Title Data Scientist Machine Learning Target Location US-WA-Edmonds Email Available with paid plan Phone Available with paid plan	20,000+ Fresh Resumes Monthly     View Phone Numbers     Receive Resume E-mail Alerts     Post Jobs Free     Link your Free Jobs Page     ... and much more Register on Jobvertise Free
Search 2 million Resumes Keywords: City or Zip:	Related Resumes Machine Learning Data Scientist Seattle, WA Data Scientist Machine Learning Bellevue, WA Data Scientist Machine Learning Seattle, WA Machine Learning, Data Scientist Seattle, WA Machine Learning Data Scientist Seattle, WA Data Scientist Seattle, WA Data Science Scientist Redmond, WA

Candidate's Name
Data Scientist, Machine Learning EngineerSeattle WAEMAIL AVAILABLEPHONE NUMBER AVAILABLESKILLSData Science :MLOps/MLSecOps, Neo4j graph database, genetic algorithm, docker, scikit, pandas, natural language processing, machine learning, deep learning, decision trees, neural nets, data analysis techniques, isolation forest, tensor flow, keras, pytorch Software Development :languages : Python, Cypher, C/C++, shell scripting, javascript, Java, Assembler, SQL, Unreal Engine Blueprint, Visual Basic, Scala, xPath, Lisp, awk processes : MLOps, Test Driven Development (TDD), DevOps/CICD, Scalable Agile Framework (SAFE), ScrumSystems : MLFlow, github, graph database, language interpreters and compilers, hardware drivers, embedded systemsPerformance Test Engineering :QA Software Performance Test, LoadRunner, Vugen, Microservices (SOAP, REST, json, Web APIs), Software Development Lifecycle (SDLC), Data Analysis and Reporting, Jenkins, javascript analysis and simulation, open source javascript libraries, external DLLs Linux/Unix System Administration :Unix, Linux, AIXLeadership :Documentation, Team Lead, Training, Strategic Planning and Direction, Presentations to technical and business audiencesRELEVANT EXPERIENCEData Scientist/ML Engineer, Resilient Solutions 21, 04/2022  02/2024 Architected and developed python API driver to provide realtime, production interface between a python web application and Neo4j database. Developed Entity Recognition system built on fuzzy and cosine similarity matching Developed anomaly detection applications that used a variety of techniques including multivariate regression and isolation forest Developed genetic algorithm to identify combination of factors in multi-variate regression model to produce the highest scores, outperforming other methods Implemented recurrent neural net for simple image recognition Built and deployed in production MLOps/MLSecOps procedures, processes and python framework from scratch Built facilities to receive streamed data and update the ML model's inputs Implemented data drift detection on data stream Implemented model drift detection and automatic retraining with MLFlow Secured the data and model throughout all stages of the pipeline Built dashboard to monitor the status of the production MLSecOps pipeline from end to end using StreamlitSenior Application Performance Test Engineer, Boeing, 05/2011 - 01/2019 Developed custom code in C/C++ to simulate a variety of conditions Collected gigabytes of detailed data regarding application performance Used AI (Genetic Search) data mining techniques to extract relevant information Modeled and projected the behavior of the application under a variety of conditions using statistical methods on the data collected in the mining Presented results to internal and external decision makers and executive leadership teamsSenior Application Performance Test Engineer, JP Morgan Chase (formerly Washington Mutual), 1/2003 - 10/2009 Developed custom code in C/C++ to create over 100 concurrent, non-discrete simulations recreating production conditions Developed an extensive function library that reduced simulation development time by 80%, and increased productivity by 300% Created statistical templates to speed data analysis by 85% Created parametric model to estimate simulation development time, accurate to 10% Senior Unix System Administrator, Department of IT, City of Seattle, 3/2000 - 3/2002 Developed and deployed Reinforcement Learning application for monitoring and failure prediction application that increased system up-time from 92% to 99.9+% Independent Researcher, 2/2024 - present Implemented convolutional neural network similarity classifier to classify raw sound data at 44.1khz Built custom GUI interface for Neo4j database that provided full CRUD operations of nodes and relationships Worked through the RWKV implementation code to get a working RNN based LLM that is faster and requires less memory than a GPT model. (url provided on request) In process of building an LLM from scratch, including tokeninzing the corpus all the way through engineering the prompts.EDUCATIONData Science Immersive, General Assembly, June 2019 - August 2019 Master of Science in Computer Science, University of South Florida, 1/1995 to 6/1998 Bachelor of Science in Computer Science, Georgia College, 9/1988 to 3/1990 Associates Degree in Computer Science, Macon College, 9/1984 to 8/1986