Education
MS Applied Statistics (Concentration: Machine Learning)
Aug 2016 - Dec 2018
BA Economics (Minor: Mathematics)
Aug 2010 - May 2015
Skills
R: Pyth Pythoon: SQL: Micros Microsoft oft Excel: Excel: BI Tools: Predictiv Predictivee Modeling:
Tidyverse, ggplot, CARET, nnet Scik Scikit it-L -Lea earrn, Panda andas, s, NumP umPy/Sc y/SciP iPy y, Jup Jupyter yter No Notteboo ebooks ks MySQL, Oracle SQL Functio unctions, ns, pivots pivots,, visual visualiza izatio tions ns Business Ob je jects, Power BI Supervised Supervised and unsupervised unsupervised machine machine learning, learning, statistic statistical al modeling
Work Experience Experience
Business Analyst •
Aug 2016 | Aug 2018
is one of the nation nation's top mortgage lenders. Wrote SQL queries, wrangled data, and designed BI reports to address business needs
• Incorporated
fuzzy matching techniques in R to assist executives in merging spreadsheets with imperfect keys, previously an inconsistent and manual process
• Recognized
for utilizing innovative SQL queries (nested select’s, groupby, max/min, rank) to tackle difficult data requests
• Designed
an automated report that ranked zip codes with a high proportion of late payees, frequently implemented for outreach programs to reduce late payments
Data Analyst •
May Ma y 2015 | Aug 2016
is a tech tech start-up start-up that functions functions similar similar to Angie’s Angie’s List. Primary Primary role involv involved ed data wrangling and data analysis with Excel to pinpoint business inefficiencies
• Parsed
and visualized customer complaints using Python and Excel to determine common issues per month, which were addressed and reduced for the following month
• Incorporated
a state map in Power BI with contractor geolocations. This was used to determine closest contractors to new orders and improved on-time percentages by 20%
Projects
Predicting Drug Rehab Success Python
Classified drug rehabilitation patient outcomes (best model: neural network, 80% accuracy.) Improved model performance with dimensionality reduction, feature engineering, and missing value handling Predicting Song Genre R
Scraped lyrics and song info from Genius.com and classified songs into genres using term document matrixes matrixes and engineere engineered d features features (text mining, sentiment sentiment analysis.) analysis.) Best model: random random forest, forest, 65% accuracy NDA Classified potential returning customers on an imbalanced and confidential travel tour dataset (best model: neural network, 70% sensitivity) Tour Guide Customer Classification Python
Predicting Crime in Chicago R, SAS
Predicted the probability of different crimes occurring in various Chicago neighborhoods using multinomial logistic regression Certifications
MIT 6.00.1x: 6.00.1x: Introduct Introduction ion to Computer Computer Science using Python Python
edX
Certified Predictive Modeler Using SAS Enterprise Miner 14
SAS
