Resume

Contact me at: emcclave@alumni.cmu.edu | eganmcclave.com | (917) 370-3612

EDUCATION

Carnegie Mellon University - Pittsburgh, PA

  • M.S., Statistical Practice (GPA: 4.0)

  • B.S., Mathematical Sciences - Concentration: Operations Research and Statistics

PROFESSIONAL EXPERIENCE

Guidehouse – Data analyst (washington, dc / Sept 2019 – present)

DuckerFrontier – Capstone Project Consultant (Pittsburgh, PA / Jan-May 2019)

  • Completed a team-based consulting project to help DuckerFrontier effectively forecast its subscription renewal

  • Used SQL to aggregate and clean 5 years of subscription data, and imputed missing values as necessary

  • Applied Logistic Regression, Random Forest and various survival analysis models in R to determine key performance drivers and to predict contract renewal rates

Carnegie Mellon University – Teaching Assistant (Pittsburgh, PA / Sept 2018-May 2019)

  • Provided 4 semesters of teaching assistance in multiple advanced statistics classes with topics that include linear/nonlinear regression, predictive model-checking, uncertainty estimation, graphical models and causal inference

U.S. Army Research Lab – Summer Intern (Adelphi, MD / Jun-Aug 2018)

  • Led a 4-member team on a project that predicted network connectivity issues using Machine Learning methods

  • Designed a Recurrent Neural Network using TensorFlow in Python to prevent information loss

U.S. Army Research Lab – Summer Intern (Adelphi, MD / Jun-Aug 2017)

  • Helped deploy an Internet of Things (IoT) network of multi-modal sensors to collect motion data

  • Detected anomalous activities using unsupervised learning techniques in R on unstructured data

  • Co-authored a research report on autonomous classifying sensor network

WebSubstance – Summer Intern (Sterling, VA / Jun-Aug 2016)

  • Assisted a digital marketing agency with web design and search engine optimization services

COURSES & PROJECTS

Data over Space and Time – Evaluation of Wind Turbine Locations

  • Built models to analyze interdependent data over space and time

  • Identified the ideal locations on Massachusetts Coast that maximize potential wind power from wind turbines with geospatial Kriging technique in R

Experimental Design & Time Series – Analysis of London Mortality Rate

  • Studied time series models and experimental design (randomization, multiple hypothesis testing, etc...)

  • Utilized Time Series Regression, Vector Autoregressive and Neural Network models in R to analyze the relationship between the environmental factors and death rates in London in early 2000s

Statistical Computing – Detection of Anomalous Objects

  • Studied data structures and algorithms, databases, parallelism and effective programming practices

  • Implemented Isolation Forest (anomaly detection algorithm) in Python to estimate speed of vehicles from a video

Data Mining – Classification of Genomics Data

  • Learned to apply statistical methods to discover structure and make predictions from large, complex data sets

  • Utilized unsupervised and supervised learning techniques to analyze and classify large scale genomics data

TECHNICAL SKILLS

R, Python, SQL, Bash, Git, Hadoop, SAS, LaTeX, HTML5/CSS, MATLAB, Mathematica, Google Analytics, Tableau

ADDITIONAL COURSEWORK

Applied Linear Models, Numerical Methods, Probability Modeling, Statistical Methods of Epidemiology, Machine Learning

ACTIVITIES & INTERESTS

Eagle Scout, Data Science competition, Podcasts, Manga, Parkour, Reading, Cycling, Running, Volunteering