Resume

• 2014

  English

  Doctor of Philosophy (Ph.D.)

  I conducted multiple studies throughout my time at Saint Louis University in collaboration with my colleagues and supervisors. I also completed additional coursework in statistics earning me a secondary concentration in quantitative methods.

  Social Psychology; Quantitative Psychology

  Saint Louis University

  3.95 GPA

• 2012

  Master of Science (M.S.)

  I conducted my own research involving political policy and social rejection and extended research conducted during my undergraduate education. I worked as a graduate research assistant

  during which time I furthered my background in research methods and statistics.

  Psychology

  Vice-President of Road Running Emus

  Eastern Michigan University

  3.83 GPA

• 2010

  Bachelor of Science (B.S.)

  In addition to being an undergraduate psychology student at Eastern Michigan University

  I also served as a leader in The Road Running EMUs and Active Minds student groups. I authored a senior thesis based on my own research and conducted all relevant statistical analyses. I also minored in creative writing.

  Psychology

  President of Active Minds at EMU

  \nRoad Running Emus

  \nPsi Chi

  \nPsychology Club

  \nActive Minds at EMU

  Eastern Michigan University

  3.95 GPA

• 2008

  Associate of Arts - AA

  Enrolled in collegiate level courses during high school. Completed the requirements for an Associates of Arts degree at the same time as earning my High School Diploma.

  General Studies

  Magna Cum Laude

  \nPhi Theta Kappa

  Lakeland Community College

  3.75 GPA

  Social Psychology of Justice

  Social Psychology

  Psychometric Theory

  Ethics and Professional Issues

  Psych Statistics 1 (Univariate Statistics)

  Programming for Data Science (Python)

  Psych Statistics 2 (Multivariate Statistics)

  R (Statistical Computing for Psychological Research)

  Developmental Psychology

  Learning

  Personality Theory and Research

  Human Diversity

  Non-parametric Statistical Analysis

  Cognition and Affect

  Advanced Physiological Psychology

  Advanced History and Systems of Psychology

  Research Design

  Social Cognition

  Memory and Cognition

  Cognitive Development

  Data Manipulation in R with Dplyr

  DataCamp

  Introduction to R Course

  ARIMA Modeling with R

  Machine Learning in R: Classification

  Machine Learning with the Experts: School Budgets Course

  Machine Learning Toolbox

  Intro to SQL for Data Science

  Deep Learning in Python

  Intermediate Python for Data Science

  Introduction to Time Series Analysis

  Machine Learning with Python Skill Track

  Machine Learning in R: Regression

  Introduction to Machine Learning

  Introduction to Spark in R using sparklyr

  Importing Data Into R (Part One)

  Intro to Python for Data Science

  Data Visualization in ggplot2 (Part 1)

  Forecasting Using R

  Machine Learning with R Track

  Manipulating Data Frames with pandas

• Brendan | Data Science Profile

  Predict categorical and numeric responses via classification and regression

  and discover the hidden structure of datasets with unsupervised learning. DataCamp offers interactive R

  Python

  Sheets

  SQL and shell courses. All on topics in data science

  ...

  Brendan | Data Camp Accomplishments

  I served as Vice President for a student organization at Eastern Michigan University. This organization was an organization for people who enjoyed running

  and this organization often held running events

  such as group runs and races. In my role

  I served to increase membership and help plan events such as campus wide races.

  Road Running Emus

  President

  Active Minds is part of a larger national organization that seeks to reduce stigma toward mental health issues and increase awareness of mental health issues. I helped found and served as the first president of the new local chapter called Active Minds at EMU

  and during my tenure I increased group membership and commitment to the new organization and the cause.

  Active Minds at Emu

  Social Media

  Supervised Machine Learning

  Data Visualization

  Quantitative Research

  Microsoft Office

  Research

  Python (Programming Language)

  Big Data

  SQL

  Microsoft Word

  SPSS

  PowerPoint

  R

  Tableau

  Statistics

  Psychology

  Microsoft Excel

  Research Design

  Data Analysis

  Data Modeling

  Free-will and political policy support

  Authored research paper which identified how perceptions of the average person’s free will predict political policy support. Manuscript is in currently being finalized for publication.

  Confidence in Institutions

  Primary investigator and data analyst on an ongoing research project that assesses how positive and negative examples of government and private-run healthcare institutions influence support for either government or private healthcare institutions.

  Framing and Political Policy Support

  Primary investigator and data analyst on a research project examining how framing and risk predict support for political policies such as increasing the minimum wage

  social security and prison privatization

  and counter-terrorism programs.

  Immigration and tax policy

  Primary investigator and data analyst on an ongoing research project which explores how emotions toward immigrants and the wealthy as well as perceptions of their blameworthiness for the current economic circumstances predict support for immigration and taxation policies.

  Molinar

  Ph.D.

  Brendan

  Molinar

  Ph.D.

  Equifax Workforce Solutions

  Eastern Michigan University

  Boeing

  Saint Louis University

  Greater St. Louis Area

  •\tDefined algorithms in R and Python to employ multiple unsupervised machine learning techniques - such as mixed variable cluster analysis

  dimension reduction

  and principle components analysis - to identify

  classify

  and resolve data integrity issues and improve business outcomes

  such as improved supply chain forecasting.\n•\tEngineered supervised machine learning algorithms - such as random forest

  decision trees

  neural networks

  and logistic regression - to correctly classify inventory based on established exception criteria. Evaluated and selected models based on best fit criteria

  defined algorithms to accurately impute missing values in large data sets.\n•\tHeaded large scale data cleanup effort and coordinated with a large team of people to resolve data issues. Performed data preparation and conducted big data analytics using programs such as R

  Python

  SQL

  Access

  Excel

  and Spark.\n•\tAutomated the reporting and assessment of program performance metrics and data cleanup metrics to improve employee efficiency on the program

  which lead to better business results\n•\tEmployed ARIMA and other predictive modeling techniques to forecast future supply demands and provide analytic solutions for the Boeing Company\n•\tConstructed detailed data visualizations through programs like R (e.g.

  ggplot2)

  Python

  and Excel to convey clear patterns to upper management\n

  Data Analyst/Supply Chain Specialist (Contractor)

  Boeing

  Greater St. Louis Area

  •\tEnhanced and expanded data pipeline crucial to vital business needs integrating code written in R

  SQL

  Python

  and SAS. Helped enable the automation of the pipeline\n•\tCollaborated with a team of data scientists and engineers

  in an agile framework

  to produce a product showcasing important business metrics to external customers \n•\tInterrogated data utilizing machine learning methods (e.g.

  random forest

  decision trees

  logistic regression) to understand current users of business service. Applied machine learning model to identify and illustrate opportunity to important stakeholders\n•\tPrototyped new visualization designs in R

  Python

  and Tableau

  and designed and implemented programs to scale out such visualizations in PySpark (Spark) and Hive (SQL)\n•\tEnvisioned and created dashboards in Tableau and Python's Bokeh library to showcase important business metrics to both internal and external customers\n•\tDiscovered important patterns to define business strategy and execution through unsupervised and supervised forms of machine learning - such as cluster analysis

  decision tree models

  random forest

  gradient boosted trees

  etc.

  Data Scientist

  Equifax Workforce Solutions

  Greater St. Louis Area

  •\tAdvised Boeing on the creation

  construction

  and implementation of dashboard tools that could be levied on big data across the enterprise\n•\tAnalyzed and discovered trends in fleet health maintenance data using analytical tools

  such as Python

  pandas

  SQL

  and various forecasting and time series methods in R\n•\tDesigned interactive dashboard via the Shiny and Shinydashboard packages in the R language to visualize

  download

  and assess data on asset data exceptions and errors pertaining to current inventory position\n

  Data Analyst/System Support Technologist (Contractor)

  Boeing

  Greater St. Louis Area

  •\tDetermined

  defined

  and deployed predictive/prescriptive analytic solutions to meet business objectives

  such as the construction of new analytical tools and dashboards\n•\tIdentified best fit methods for internal clients

  defined algorithms

  validated and deployed models to improve business results and overall operating margin\n•\tPerformed necessary data preparation

  feature extraction

  data manipulation

  and enhancements to models in order to employ advanced analytical techniques

  such as machine learning\n•\tInvestigated and deployed new analytic methodologies and technologies to improve existing procedures and reduce overall operating costs of company\n

  Data Scientist

  Boeing

  Ypsilanti

  MI

  •\tConstructed and validated surveys for the purpose of data collection and leveraged scientific methods and statistical analyses to assess the results of experimental manipulations on behavioral and attitudinal reactions\n•\tLeveraged generalized linear models (e.g. ANOVA

  t tests

  etc.)

  mediation

  and moderation analyeses through software

  such as SPSS and Excel

  to evaluate different datasets

  test theories

  test model performance

  and produce valid findings to guide future research. Recounted findings to general audiences.\n•\tPreformed data preparation and data cleanup on a variety of datasets to optimize and enhance model performance and provide actionable results\n•\tLed a lab research group and collaborated with numerous associates in conducting an experimental behavioral study\n

  Research Assistant

  Eastern Michigan University

  Greater St. Louis Area

  •\tProject manager who led the development

  analysis

  and presentation of several research projects. Inspired

  coached

  managed

  and assessed research projects from multiple independent teams of researchers.\n•\tCreated and evaluated structural equation models

  generalized linear methods (e.g. linear regression

  ANOVA

  t-tests

  etc.)

  and non-parametric methods (e.g. kolmogorov smirnov

  chi-square) to test research hypotheses. Used statistical techniques to identify optimal model fit and improve model performance based on statistical tests and modification indices.\n•\tPerformed data cleanup and formatting in programs such as R and SPSS in and by using model-based and machine learning imputation methods (e.g.

  random forest

  knn

  etc.). Evaluated descriptive statistics to verify statistical and model assumptions.\n•\tDefined algorithms in R to run supervised machine learning analyses

  such as regression analyses. Also used R and SPSS to test model fit of structural equation and lagged models

  conduct dimension reduction

  assess mediation and moderation effects

  and create intricate data visualizations with ggplot2 in R\n\n

  Research Assistant

  Saint Louis University