PDF Version
ANDREW REECE
andrew.reece@gmail.com
andrewgarrettreece.com
github:andrewreece
stackoverflow:andrew_reece
OVERVIEW
  • Full-stack data scientist: 6 years experience in analytics, research, and product development
  • Skill set combines statistical analysis, software engineering, and scientific research
  • Proven experience in both client-facing and engineering roles on solo and team projects
  • Skill highlights: Machine learning (Python, R), big data analytics (Spark), front-end (D3, Flask)
TECH SKILLS
  • Strong
  • Python, R, JavaScript (D3, jQuery), AWS (EC2, EMR, S3)
  • Proficient
  • Golang, Docker, Spark, Kafka, Flask, SQL, PHP, Bash
  • Basic
  • Cython, OpenCL, Perl, Matlab, Angular
EXPERIENCE
2017
MEMBER OF TECHNICAL STAFF
StackRox, Inc.
  • Led project to develop automated analysis of machine learning performance and threat detection efficacy
  • Contributed to engineering efforts across the full stack: back end, UI/UX, API development, machine learning
  • Helped launch company’s first enterprise-ready product
2016
DATA SCIENCE CONSULTANT
Appuri, Inc.
  • Used machine learning methods to predict customer churn
  • Provided analytics-driven solution consulting to company execs & clients
2016
DATA SCIENCE & STRATEGY CONSULTANT
Shortlist, LLC
  • Created analytics pipeline for early-stage talent acquisition startup
  • Defined strategic initiatives for near-term data/analytics goals
2015 - 2016
DATA SCIENCE CONSULTANT
DrivenData, Inc.
  • Generated predictive modeling solutions for multi-sector client base
  • Lead data science trainings for clients
2015 - 2016
CYBER SECURITY DATA SCIENCE INTERN
Rapid7, Inc.
  • Applied machine learning methods to identify email phishing attacks
  • Generated 3 patent applications in first 3 months on the job
  • Lead project placed on Q4 corporate roadmap for product integration
2015
DATA SCIENCE CONSULTANT
Exceptional Lives, LLC
  • Provided analytics, software design consulting to non-profit group building crisis response app for caretakers of disabled individuals
2014
DATA SCIENCE FELLOW
Chicago Department of Public Health
  • Created predictive model to reduce residential lead-poisoning hazards
  • Helped City of Chicago secure $3.9M federal grant from US Dept. of Housing & Urban Development for lead prevention data science project
  • Published findings in peer-reviewed 2015 KDD Conference Proceedings
2013 - 2016
STATISTICS TEACHING FELLOW
Harvard University
  • Lead weekly class sections for over 100 graduate and undergraduate students in research statistics, machine learning, and data visualization
  • Authored training materials for introduction to data analysis using R
DATA SCIENCE PROJECTS
GaugingDebate
Real-time sentiment tracking of 2016 US Presidential debates
  • End-to-end analytics pipeline: streaming Twitter data, parallelized sentiment analysis, dynamic front-end web app
  • Tech: Spark, Kafka, Flask, Plotly, Twitter API, AWS
HelloMarc.us
Hollywood box office prediction with Natural Language Processing
  • Latent Dirichlet Allocation (collapsed Gibbs) on 40k+ movie reviews provided features for machine learning algorithm to predict box office success, coded MCMC algorithm from scratch
  • Tech: Python, Pandas, Scikit-Learn, Flask, Bootstrap, D3
SEE:Net Graph
Social network graph with continuous-tracking sensor data
  • Graph analysis and visualization web app used MIT Reality Mining experiment dataset; demonstrated emergence of social networks with interactive time series dashboard
  • Tech: D3, jQuery, R, Python, jQuery
AWARDS & RECOGNITION
2016
Kaggle.com Master
Ranked in Top 1% of all 475,000+ worldwide data science competitors
2016
Sackler Scholar of Psychobiology
Research fellowship awarded for developing contributions to clinical science
2014
Eric & Wendy Schmidt Data Science for Social Good Summer Fellowship
Awarded for excellence in data science and commitment to social causes
HARVARD COURSEWORK
  • Machine Learning: Mathematical foundations in Bayesian modeling and optimization
  • Monte-Carlo Methods & Stochastic Optimization: Range of MCMC and optima methods
  • Data Science: Full-stack predictive analytics (scraping, munging, modeling, visualization)
  • Data Visualization: Design principles and interactive visualization with D3
  • Computing Foundations for Computational Science: Parallel, GPU optimized computing
  • Learning From Big Data: Team Kaggle competitions in big data, machine learning
  • Introduction to Probability: Random variables and distributions, basic combinatorics
PATENTS
Identifying Malicious Identifiers
Pending, PCT: 15/177,555
  • Algorithm for identifying malicious links in emails
Classifying Locator Generation Kits
Pending, PCT: 15/200,530
  • URL parsing engine for discovering malware attack campaigns
Neutralizing Malicious Locators
Pending, PCT: 15/196,072
  • Cyber counter-attack for triggering shutdown of malicious websites
EDUCATION
2017
PhD
Psychology | Computational Science
Harvard University
Dissertation: “Changes in social media behavior predict clinical diagnosis”
GPA: 3.89