Leading development and deployment of cybersecurity machine learning (ML) solutions for Navy support systems.Developed a cybersecurity ML triage system to prioritize review and patching of CVEs.Developed a knowledge graph to solve a long standing record linkage problem between multiple Navy fleet readiness checks to solve issue tracking. This project was a leading priority to solve throughout multiple Navy offices.

Building machine learning applications to detect injury categories (e.g. fractures vs sprains) from injury text reports using NLP. Our customers require a level of compliance with the federal government so our application alerts them when an injury is severe enough to be reported to the US Department of Labor.Using natural language processing (NLP) to extract important injury informationUsing gradient boosting trees to predict recordability status to follow compliance with the Occupational Safety and Health Administration (OSHA)Managing deployment for ML applications and AWS services

Served as internal data science consultant to multiple internal business units. Worked to develop new data pipelines and updated risk underwriting models. Expertise in Property and Casualty.Built multiple tools to reduce manual work for underwriters and introduce automation of simple tasks reducing the hours wasted by underwritersAmongst other projects, updated a predictive model to identify likelihood of high auto insurance claim frequency. Improved previously existing model’s performance by 10% through the use of gradient boosting trees

Worked closely with product managers to develop analytics services and solutions for the auto insurance market. Developed new machine learning methodologies to detect injury severity from auto collisions and accidents using images and metadata. Utilized CNN’s to build computer vision models that detect types of collisions and estimate injury severity.Lead author on multiple patents -- increased intellectual property at Mitchell International with new machine learning technology.- Using Genetic Algorithms and Monte Carlo methods for medical network optimization- Using neural networks (CNN) to predict likelihood of injury from vehicle accident images/metadata- Using survival regression models to estimate time to recovery from vehicle accident related injuries- Applying decision trees to recommend medical treatment reviews to minimize medical costs

A fully developed graduate-level course on Monte Carlo methods open to the public. I simplify much of the work created leaders in the field like Christian Robert and George Casella into easy to digest lectures with examples. The target audience is anyone with a background in programming and statistics. In this course, students tackle problems of generating random samples from target distributions through transformation methods and Markov Chains, optimizing numerical and combinatorial problems (i.e. Traveling Salesman Problem) and Bayesian computation for data analysis.https://www.udemy.com/introduction-to-monte-carlo-methods/

Analytic scientist working in the Analytics Engineering Group and Modeling Services as part of the Falcon Fraud services.- Built an Apache Spark cluster for processing and analyzing large data sets- Lead the adoption of Apache Spark to replace older tools- Data mine clients’ data to identify and explain current fraud transaction patterns- Update and develop data mining tools used in fraud trends analysis- Assist modeling teams evaluate data quality and investigate data integrity issues- Assist with clients as they upgrade models by performing production data validations and monitoring fraud score distributions to evaluate overall performance of fraud detection

As data science analyst, I assisted consultant clients identify solutions to business research questions. I worked closely with clients to develop statistically sound data analysis methodologies, and provide timely results to drive future business strategies. In collaboration with statistical analyses, I also provide solutions for big data analytics, ready to build high-performance, highly-available and scalable distributed systems.· In-house Statistician and lead statistical modeler· Longitudinal statistical models (GEE, Mixed Effects, Hierarchical models)· Proficiency in high-performance, multi-core programming· Able to extract large data sets and perform complex manipulations using R/Python· Conduct statistical analyses using R for tracking and evaluating deviations in client's core KPIs· Produce, interpret and present statistical findings for clients and stakeholder· Practical knowledge of Hadoop and Spark ecosystem· Experience designing and building highly-scaled distributed systems and web services· Leverage advanced analytics to enhance existing client’s products and deliver new impactful features for customers.· Knowledge about trend analyses, Bayesian statistics, multivariate statistics, sampling, Generalized Linear Models, Monte-Carlo simulations, bias reduction, indirect estimation, data aggregation techniques for feature engineering.

As research analyst for a grant funded program at Palomar College, I took charge in leading the way to a more data driven approach in evaluating outreach and interventions programs, and suggesting programs that positive data-backed results. In addition, I lead the training sessions for technical training for new staff. · Design and administer surveys using Qualtrics survey software · Assists in research, and program evaluation for mandatory U.S. Department of Education annual progress reports · Provide support, technical assistance, and consultation related to research reports, data analysis, and surveys for special research projects to administrators and departments. · Experienced in grant research and writing · Regular use of private and public online databases (e.g. FactFinder, National Student Clearinghouse) to obtain needed data.

As first recipient of the LA Center Fellowship, I lead a geospatial analytics project to analyze natural resource demand for the highest urbanized national forest. Utilized R and ArcGIS to measure and display demand. · Survey methodology and statistical analysis · GIS and Spatial Analysis of the S. California Agency of Governments region · Conduct literature and background research for qualitative and quantitative projects

As intern, I lead data-mining and geospatial analytics using R and GIS software to identify new target locations for future marketing strategies. · Leverage large data sets from internal and external databases to mine for key insights · Defining business cases and solutions to business problems · Improved overall business analytics methodology · Conduct geospatial analytics and predictive analytics in R