Working as a Data Scientist in Grand Rapids, MI

I work as a consulting data scientist with expertise in R. I've worked with many cutting edge R packages for both personal interest and professional development.

When it comes to R programming, I find my joy practicing data manipulation using the tidyverse. I consider myself an expert in managing data pipelines with the targets package in R, creating visualizations with ggplot2 and plotly, preparing reports using R Markdown and Quarto, along with creating interactive dashboards using flexdashboard and shiny.

Helped develop a series of training videos to help both experienced and new programmers learn R, with a focus on clinical SAS programmers. This includes a gentle itroduction to R, RStudio, and the tidyverse, along with the basics of data manipulation, data pipeline management, creating R packages, troubleshooting and debugging, and data visualization.

This role encompasses the development of content, recording and editing videos, hosting live learning sessions, holding virtual office hours to assist learners through their learning journey, along with analyzing and reporting on the curriculum progress that learners make on a week-by-week basis.

Working as a consulting data scientist, I have come into contact with many research projects and statistical programming languages. I have expertise in R, but am confident using SAS, SAS Enterprise Miner, SQL, and the H2O Machine Learning platform.

In R, I have experience with the following:

• The tidyverse family of packages
• Building and documenting R packages with roxygen2, devtools, testthat
• Maintaining data pipelines using targets
• Building predictive models with sparklyr and h2o
• Creating interactive visuals with ggplot2 and plotly
• Creating static and interactive reports with R Markdown and Quarto
• Creating interactive dashboards using flexdashboard and shiny
• GitHub version control
• Creating web crawlers and using web based APIs with httr

Working as a consultant with Microsoft, through Experis, I helped bring a predictive modeling process from ideation to production. The final product was a collaboration of technologies, including Azure Data Lake Store, Azure HDInsight, Spark, H2O Machine Learning, Microsoft Machine Learning Server, and R Studio Server.

As a small team, we began by comparing the capabilities of SAS and R for our use case. We developed a process of collecting and organizing hundreds of millions of user records, building training data based on specific marketing campaign requests, building classification models and selecting champion models to be used for scoring the master set of user records. We would deliver a final list of the users most likely to respond to a particular campaign.

I assisted in the development of the R scripts that would flow from creation of training data, to building models, to scoring the master user database. To accomplish this we made extensive use of the tidyverse family of packages, along with the sparklyr, and h2o packages.

Working in the context of a spark cluster was integral in developing my understanding of working with big data, and the trial and error that comes along with optimizing the processing power of building machine learning models.

Worked with a small team within the internal research department. Here we met with care providers to collect research questions regarding both patient care and business management. We developed plans for analysis to find answers for these questions. Our research ranged from developing levels of care assessment models to financial forecasting.

In the course of our research we made use of R, conducted reviews of published literature relating to our questions, and consulted online documentation and communities to provide insight into how apply analysis techniques in R.

In the Office of the Vice Provost for Health, I assisted the project development manager in writing grants, inviting speakers, preparing presentations, and hosting seminars regarding inter-professional healthcare.

In addition to this, I lent my statistical knowledge to an ongoing study being conducted by the Office of the Vice Provost for Health. The study sought to compare weight loss interventions within an urban health care setting. I was responsible for entry and exit interviews of the volunteers, along with the recording of the data into Excel, and basic reporting with R and rmarkdown.

At the GVSU statistical consulting center, I worked within a small team of graduate and under-gradudate students to meet with individuals or teams seeking assistance in developing their research projects. This position helped me to foster technical discussions regarding statistical theory and application, and to understand how to best convey these complex ideas to the clients who generally had little experience with mathematics and statistics. The programs we used depended on the client and included SAS, R, SPSS, and Stata.

As a tutor in the GVSU stats lab, I worked among a team of other grad and undergrad students to assist introductory stats students with their coursework.

I worked to ensure accurate recording of incoming donations, along with balancing actual with expected donation amounts. Within the database, I would record data, resolve errors, and remove duplicates within the PledgeMaker database management system.

In the Columbia University Summer Institute for Training in Biostatistics, I worked within a team consisting of a grad student, a doctoral student, and a tenured professor of sociomedical sciences. As a summer intern, I took part in the ongoing research of the doctoral student of the professor. The other grad student and I posed our own research questions, analyzed the existing collected data using a statistical techniques we were unfamiliar with, and presented our results to our internship cohort.

This position required me to certification from the Institutional Review Board and from the Responsible Conduct of Research boards through Columbia University.

• Work with customers daily, always employing a respectful and helpful manner
• Make sure costs are properly being registered and taking care of any price discrepancies
• Field incoming calls, ensuring callers reach the person or conclusion they are seeking
• Respond accurately and articulately to customer inquiries regarding Meijer policy
• Often trusted with coordinating the service area, balancing customer flow with employee breaks

• Mu Sigma Rho Statistical Society
• Mid-West SAS Users Group 2014 Scholar
• Columbia University Institute for Training in Biostatistics

Worked with a small team of students under the direction of Dr. Ana Abraido-Lanza researching healthcare satisfaction survey responses among women in the New York City area.

Amstat News, The Paradox of Choice: Statistical Software Packages