Ben Barnard

Data Scientist

Wells Fargo


Ben Barnard is a Data Scientist for Wells Fargo and a Statistics Instructor for the University of South Alabama. His research interests include Bayesian methods, statistical programming, teaching statistics, and using common sense in data science. He currently leads the development of several R and python packages for internal Wells Fargo use. Outside of work Ben likes to spend time with his family, working on the house, competing in triathlons, and speaking at conferences.


  • Bayesian Methods
  • Statistical Programming
  • Data Science Consulting


  • PhD in Statistical Sciences, 2018

    Baylor University

  • MS in Statistical Sciences, 2016

    Baylor University

  • MSEd in Exercise Physiology, 2013

    Baylor University

  • BS in Exercise Science, 2011

    University of Texas at Arlington



Data Scientist

Wells Fargo

May 2018 – Present Remote

Responsibilities include:

  • Model development lead for Human Resources allegation team workforce capacity and turnover models.
  • Development lead for Human Capital Key Risk Indicators.
  • Developer of 20 internal R packages using agile development with 2-week sprints and daily asynchronous stand-ups in Skype chat groups.
  • Enterprise Product Steward for R and Rtools monitoring intake and maintenance of new versions, and mitigating security vulnerabilities for ∼2500 installs across the enterprise.
  • Led transformation of team to use software development life-cycle appropriate tools such as GitHub, Jenkins, and Artifactory.
  • Led the creation of Human Resources Modeling Center of Excellence that centralized Human Resource model development (100+ automated production models), and streamlined recruitment of new data scientists.

Instructor in Statistics

University of South Alabama

Aug 2017 – Present Mobile, Alabama

Responsibilities include:

  • Coordinator for design and instruction of Applied Statistics for Health Sciences courses.
  • Implementation of Quality Matters and Team Based Learning instructional methods in undergraduate statistics sequence of courses.
  • Directed consulting and student led research for tumor risk and classification models using neural network, Bayesian and ensemble based methods.
  • Course design in Sakai, Blackboard, and Canvas learning management systems.

Graduate Assistant

Baylor University

Aug 2011 – Aug 2017 Waco, Texas

Responsibilities include:

  • Developer for conversion rate key performance indicators on graduate applications.
  • Model developer for student success gradient boosting machine model using XGBoost.
  • Designer of A/B testing for the Graduate School website and application pages.
  • Model developer for applicant and graduate student segmentation clustering models using k-means clustering.
  • Developer for automated reporting on applications and enrollment data using D3.js, SAS reporting studio, Shiny server.
  • Model developer for athlete injury prediction model using recurrent neural network in tensorflow.
  • Statistical analysis on paleosol composition using semi-parametric models in PROC TRANSREG.

Recent Posts

Software Packages

covTestR: Covariance Matrix Tests

Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance matrix testing described by …

likelihoodExplore: Likelihood Exploration

Provides likelihood functions as defined by Fisher (1922) doi:10.1098/rsta.1922.0009 and a function that creates likelihood functions …

rWishart: Random Wishart Matrix Generation

An expansion of R’s ‘stats’ random wishart matrix generation. This package allows the user to generate singular, …

Recent & Upcoming Talks

Automated Building and Storing Frozen Data in R Packages Using Travis and Drat.

Proposed process workflow to implement and deploy automated data repository.