• Developed, implemented, and demoed multiple Power BI dashboard for 500+ end users. These dashboards, in combination with the training on how to properly analyze, enabled multiple business units to become self-analysis sufficient • Became the subject matter expert advisor on any cost related analysis while advising leadership on significant business solutions. This includes analysis on where to cut costs and how to maximize margin in both NY and NE Jurisdictions• Utilized Data Modeling to combine actuals and quarterly forecast key measurables and metrics to provide instant analysis on key performance metrics• Replaced missing FTE output due to sudden attrition by leveraging Excel and Power Query to automate the monthly performance monitoring process. This enabled more time for powerful analysis by reducing time spent on tedious data wrangling and compilation

Research Analyst | Research Analytics Team | Actuarial Department Aug 2018 – July 2021• Helped to maintain and develop predictive insurance risk models in SAS for multiple lines of business which have driven increases in company-wide profitability and helped to increase retention by 4%-7%• Led the monthly Commercial Lines Monitoring meeting by conducting detailed analysis and presenting a summarized analysis to senior leadership while proposing business solutions to previously unresolved business challenges • Presented research solutions to improve application system efficiencies to the Senior Commercial Lines leadership leading to a 35% increase in automated application handling volume to improve customer ease of doing business while decreasing manual application underwriting• Worked cross-functionally with the Commercial Lines Department, IT Department, and external data vendors to implement an automated process to acquire credit score data to improve customer prospecting. This led to a 100% increase in the number of policies that used credit score as a risk rating factor and helped to decrease loss ratios by 11%• Learned to build actionable data visualization dashboards in Tableau, Excel, and Power BI to concisely display data and information regarding KPIs to drive efficiency and profitability including analyzing Close Rates, Retention Rates, and Monthly/Weekly Premium• Presented critical analysis for product pricing changes in a particular product weight class. This led to the product weight segment being profitable again, while increasing application volume due to Agent’s increased trust

Data Engineer/Developer Jun 2017 – Aug 2018• Learned and utilized Informatica Powercenter© to perform enterprise-wide data integration flows for high level business partners within the company• Worked under tight deadlines to optimize data integration processes to operate autonomously through Tivoli Integrated Solutions Platform• Learned and became experienced in SQL Developer to execute Oracle’s PL/SQL procedural language to gain valuable insights from various internal databases• Effectively communicated with the Actuarial Department in order to understand their business needs for particular data integration projects• Worked effectively and efficiently with a team of fifteen IT professionals to deliver on project goals before project deadlines

Data Mining & Predictive Analytics• Supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Trees (CART), Random Forests, Neural Nets, Support Vector Machines, Logistics Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models.Statistical and Applied Regression Analysis• Collected, organized and analyzed large volumes of actual National Football League data using JMP statistical software to learn and develop conclusions on various scientific methods for data analysis including multiple regression analysis, residual analysis and model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, analysis of variance, analysis of covariance, examination of underlying assumptions, and logistic regression analysis.Probability with Applications• Mastered the theory, methods, and applications of randomness and random process. Studied Exam P concepts such as probability, random variables, expectation, discrete and continuous probability distributions, joint distributions, conditional distributions; moment-generating functions, and convergence of random variables.