I’m an experienced Data Scientist, Predictive Modeler, and Actuarial Analyst. Previously, as an actuarial analyst, I worked with a Fortune 500 client to create a predictive model for their employee’s medical expenses. This model used GLMs as well as other algorithms appropriate for frequency and severity modeling. I am currently working with an Actuarial fellow to analyze a frequency severity model of Workers' Compensation data using XgBoost to predict future claims. Software and Programming• Jupyter Notebook• Python Analysis (Numpy, Pandas)• Python Visualization (Matplotlib, Seaborn, Plotly)• Python Machine Learning / NLP (SciKit-Learn, nltk)• Python Web Scraping (Selenium, BeautifulSoup, urllib)• R (Multiple Linear Regression, GLMs, Correlation)• TensorFlow• SAS (SAS programming certification)• GithubMachine Learning• Decision Trees• Random Forest• Ada Boost• Gradient Boosted Machines• XgBoost• Boruta• MICE• Natural Language Processing• Frequency Severity GLMDatabase• SQL • MySQL