Robert Pehlman, Developer in Chapel Hill, NC, United States
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Robert Pehlman

Verified Expert  in Engineering

Data Science and Modeling Developer

Chapel Hill, NC, United States

Toptal member since August 5, 2021

Expertise
RSQLPythonData AnalysisWindows DevelopmentLinuxSASMacOS
Bio

Robert is a seasoned data scientist and has worked for Mastercard, United Healthcare, and currently, Google, where he supports the Google News app. He was also a PhD student in statistics at North Carolina State University while researching causal inference, reinforcement learning, and functional data analysis. Robert's passion is solving challenging data problems by building scalable models and engineering new features to improve model performance.

Portfolio

Google
R, Python, Data Science, Statistics, Modeling, Experimental Design
United Healthcare
R, Causal Inference, Reinforcement Learning, SQL
Mastercard
SAS, Netezza, SQL, Statistics

Experience

  • R - 10 years
  • Modeling - 10 years
  • Data Science - 10 years
  • SQL - 10 years
  • Statistics - 7 years
  • Python - 5 years
  • Causal Inference - 4 years
  • Reinforcement Learning - 4 years

Availability

Part-time

Preferred Environment

RStudio, Jupyter, Windows, Linux, MacOS

The most amazing...

...project I've completed was to estimate the causal effect of different treatments on acute lumbago and to find the optimal treatment sequence for pain relief.

Work Experience

Data Scientist

2020 - PRESENT
Google
  • Created an interpretable model to explain the following actions in the Google News app.
  • Improved product dashboards to measure ecosystem diversity.
  • Performed analysis of experimental metrics for proposed product changes.
  • Developed custom analysis templates to automate analysis of side-by-side comparisons of news ranking changes.
Technologies: R, Python, Data Science, Statistics, Modeling, Experimental Design

Data Science Summer Intern

2019 - 2019
United Healthcare
  • Implemented a methodology to estimate the causal effect of treatment for chronic low back pain (lumbago) treatments using propensity score matching and logistic regression to inform about treatment best practices.
  • Developed an interpretable recommendation algorithm for selecting the optimal treatment for lumbago tailored to individual characteristics, demonstrating an improvement over standard care in test data and using IPWE for evaluation.
  • Cleaned and validated a complicated electronic health record dataset for analyses using SQL.
Technologies: R, Causal Inference, Reinforcement Learning, SQL

Consultant

2010 - 2012
Mastercard
  • Designed and implemented the Small Business Insights engine, a data product linking small business data from multiple sources to increase sales of business banking products.
  • Developed and analyzed surveys to capture public opinion about prepaid cards.
  • Analyzed millions of credit card transactions using SAS and SQL to support consulting projects for retail banking clients.
Technologies: SAS, Netezza, SQL, Statistics

Experience

Nonparametric Gaussian Process Estimation R Package with an Application to Functional Linear Models

https://github.com/RobertPehlman/Portfolio/
This project aimed to create an R package to do nonparametric Gaussian process estimation. The estimation procedure takes irregular longitudinal data as input sparse, which are assumed to be subject to measurement error, and outputs an estimate for the covariance kernel of the process. An application of the Gaussian process estimation to functional linear models is explored. We examined the computational complexity of the algorithm under differing circumstances.

Education

2014 - 2020

Progress Towards a PhD in Statistics

North Carolina State University - Raleigh, NC, USA

2010 - 2013

Master's Degree in Applied Statistics

Penn State University - University Park, PA, USA

Skills

Tools

Jupyter

Languages

R, SQL, Python, SAS

Platforms

RStudio, Windows, Linux, MacOS

Storage

Netezza

Other

Data, Modeling, Statistics, Data Science, Causal Inference, Reinforcement Learning, Experimental Design, Data Analysis

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