Kimberley Mitchell
New York University
MA Economics 2014
Georgia Institute of Technology
BIE Industrial Engineering
SQL Server
Data Warehouse Architecture
Analytical Tools
Linux (ubuntu, centos)
Data Science
Data Acquisition & Munging
Exploratory Data Analysis
Regression Analysis
Design of Experiments (A/B testing)
Machine Learning
Data Visualization
Classification Analysis
Clustering (unsupervised) Analysis
Feature Engineering
Unitarian Universalist Legislative Ministry of New Jersey · Congregational Liason
2015 to Current

Data scientist with skills in exploratory data analysis, graphical analysis, statistics, hypothesis testing and predictive analytics. Flexible and eager to learn new domains and skills. Generates reliable solutions by combining industry insights with analytics and verification of underlying assumptions.

New York, NY
Data Scientist
Sep 2016 to Dec 2016

Metis is an intensive 12 week data science program focused on machine learning, python, statistical modeling, and visualization.
  • Completed 5 projects covering the entire data science process including inquiry formulation, data acquisition and munging, model fitting, evaluating, and interpretation, data visualization, and presentation
  • Tools used include python, jupyter notebook, scikit-learn, statsmodels, pandas, matplotlib, seaborn, beautifulsoup, AWS, MongoDB, git and github

Bridg-it LLC
New York, NY
Data Scientist
2015 to 2016

Full-time consultant responsible for predictive analytics strategy, data architecture and hands-on implementation for this Ed-Tech start-up.

  • Developed data integration strategy/database architecture for predictive analytics, data warehouse, and dashboards
  • Built logical schema for data warehouse (star schemas) and worked with DBA on physical implementation (MySQL, Orient db)
  • Created dashboard data visualizations (R, AMCharts and D3.js) including design, SQL, and JavaScript package implementation
  • Deployed nginx, Shiny, D3.js apps in Windows and linux (Centos) environments

Credit Suisse
New York, NY
Business Analyst
2004 to 2014

Consultant to full-time analyst/project manager implementing feature enhancements and maintaining a suite of homegrown web apps.
  • Provided SQL solutions for complex Control M generated Oracle relational database (RDBMS)
  • Optimized application strategy using analysis of user behavior logs and operations data using trends correlation analysis, ROI, information visualization, business analytics, etc.; Data visualization with JavaScript-based VML

G.E. Capital (Deutsche Financial Services)
New York, NY
Systems Analyst
2000 to 2003

Universal Instruments
Binghamton, NY
1995 to 2000

Predicting Poverty Extent Via Community Factors

Used classification machine learning methods (logistic regression, naive bayes, and random forest) to predict high or low levels of poverty in US counties. Created interactive data visualization of county-level US poverty rates. 

Text Mining Analysis: Data-Focused Job Listings

Used Latent Dirichlet Allocation (LDA) methodologies to identify latent topics in 27,000 job listings scraped from the job board. Naive Bayes and Random Forest algorithms were used to predict specific keywords and job locations, and verified via precision/recall metrics and confusion matrix. 

Data Seedlings in the Garden State

K Means and hierarchic unsupervised algorithms used to identify New Jersey municipal economic clusters; evaluated via silhouette score and graphical analysis.  

Assess Demand for Your Skills in Major US Cities

Real-time polling of Indeed job board to quantify demand for any skill in major US cities.