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Data Science at Instacart
I'm an easygoing guy with a strong work ethic, intense interest in a wide variety of topics, and a dedication to growing and improving both my technical proficiency and my repertoire of personal experiences. I always strive for balance in life, but I seldom let that get in the way of doing what needs to be done in order to finish a project and produce a quality product. I view everything I am responsible for doing as an opportunity to grow and learn. I realize I am human, and I will make mistakes, but I never shy away from claiming them. I know my work will be my legacy and shape my reputation, so I put my best into everything I do. Daily/Weekly Professional Goals: 1. Fully leverage my Ph.D. studies to continue advancing my knowledge and skills in Data Science. 2. Contribute to key wins for Instacart's Marketplace Growth team by producing actionable product insights from data. Intermediate Goals: 1. Obtain a high level of proficiency as a data scientist and machine learning engineer. 2. Build and maintain a professional network of competent, intelligent, and genuinely good people. 3. Engage actively in my company and my field and push the boundaries of what is being done. Overall Objectives: 1. Leave the world a slightly better place because of something I did. 2. Have an awesome family, and always be able to provide for them. 3. Help others outside my family as much as I can with the resources I am able attain. Principle to Live By: “To put the world in order, we must first put the nation in order; to put the nation in order, we must put the family in order; to put the family in order, we must cultivate our personal life; and to cultivate our personal life, we must first set our hearts right.” - Confucius
George Mason University
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2016 – January 1, 2025
George Mason University
Master's Degree, Computer Science
January 1, 2014 – January 1, 2016
George Mason University
Bachelor of Science (B.S.), Computer Science
January 1, 2011 – January 1, 2014
Shenandoah Valley Academy
Advanced Studies Diploma
January 1, 2007 – January 1, 2011
Instacart
Senior Data Scientist II
November 1, 2022 – Present
United States
UKG (Ultimate Kronos Group)
Senior Principal Data Scientist
November 1, 2021 – November 1, 2022
Capital One
Data Science Manager
September 1, 2019 – November 1, 2021
Capital One
Machine Learning Engineering Manager
July 1, 2018 – August 1, 2019
Capital One
Senior Machine Learning Engineer
April 1, 2018 – July 1, 2018
Capital One
Machine Learning Engineer
May 1, 2017 – April 1, 2018
Research Innovations Incorporated
Data Scientist
March 1, 2016 – May 1, 2017
Alexandria, VA
George Mason University
Machine Learning Researcher
June 1, 2014 – March 1, 2016
Fairfax, VA
George Mason University
Software Engineer
August 1, 2013 – December 1, 2013
Fairfax, VA
Blackbird Technologies
Junior Software Engineer
July 1, 2013 – June 1, 2014
Herndon, VA
Applerouth Tutoring Services
Tutor
June 1, 2013 – March 1, 2015
Washington DC-Baltimore Area
Blackbird Technologies
Cyber Programs Intern
May 1, 2013 – August 1, 2013
Herndon, VA
George Mason University
Peer Advisor
September 1, 2012 – December 1, 2013
Fairfax, VA
AkariTutoring
Tutor
September 1, 2012 – December 1, 2012
Fairfax, VA
Visualization of University Inter-College Migrations
January 1, 2015 – Present
During the initial exploratory phase of a new project, I was tasked with analyzing patterns of migration between colleges at George Mason University. The goal was to understand at a very high level how students move around through the various colleges, what percent of each college drops out, and what percent graduates. I developed a visualization using d3.js to accomplish this.
DBLP Data Processing Pipeline
August 1, 2014 – February 1, 2015
Build a data munging/processing pipeline to convert the 1936-2014 DBLP computer science bibliography data into formats suitable for scientific experimentation. In particular, the pipeline produces paper citation and author citation networks with textual features from paper titles and abstracts as node features. The data is suitable for experiments involving link prediction, community detection, and other network analysis problems. The pipeline is designed to be time-filterable, so networks can be generated for any range of years in those available. The codebase is entirely in Python, with sophisticated dependency management enabling generation of complex networks with a simple to use command line interface or a straightforward API.
Probabilistic Matrix Factorization in PyMC3 for Personalized Recommendations
January 1, 2014 – Present
Say I download a handbook of a hundred jokes, and I’d like to know very quickly which ones will be my favorite. So maybe I read a few, I laugh, I read a few more, I stop laughing, and I indicate on a scale of -10 to 10 how funny I thought each joke was. Maybe I do this for 5 jokes out of the 100. Now I go to the back of the book, and there’s a little program included for calculating my preferences for all the other jokes. I enter in my preference numbers and shazam! The program spits out a list of all 100 jokes, sorted in the order I’ll like them. That certainly would be nice. Today we’ll write a program that does exactly this. We’ll start out by getting some intuition for how our model will work. Then we’ll formalize our intuition. Afterwards, we’ll examine the dataset we are going to use. Once we have some notion of what our data looks like, we’ll define some baseline methods for predicting preferences for jokes. Following that, we’ll look at Probabilistic Matrix Factorization (PMF), which is a more sophisticated Bayesian method for predicting preferences. Having detailed the PMF model, we’ll use PyMC3 for MAP estimation and MCMC inference. Finally, we’ll compare the results obtained with PMF to those obtained from our baseline methods and discuss the outcome.
Google Ads - Measurement Certification
June 24, 2026 – Present
Google Ads Apps Certification
June 24, 2026 – Present
Google Ads Search Certification
June 24, 2026 – Present
AWS Certified Solutions Architect - Associate (SAA)
Amazon Web Services (AWS)
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's extensive experience across various companies (Instacart, UKG, Capital One, Research Innovations) and academic research at George Mason University demonstrates adaptability and a broad range of experiences. Their involvement in leading initiatives to improve AI PM practices and champion engineering best practices indicates a proactive and collaborative mindset. The personal projects showcase intellectual curiosity and a drive for continuous learning, which aligns well with a culture of innovation. However, the target role is 'Data Analyst' while the candidate's experience is heavily skewed towards 'Data Scientist' and 'Machine Learning Engineer/Manager', which are typically more advanced and research-oriented roles. This might indicate a potential mismatch in the expected day-to-day responsibilities and scope, as a Data Analyst role might be a step down in terms of technical depth and leadership for this candidate.
Soft Skills & Operational Fit
The candidate's resume demonstrates strong leadership, mentorship, and communication skills through their roles in managing teams, presenting to executives, and leading training sessions. Their experience in agile methodologies and improving AI PM practices suggests a good operational fit for structured development environments. The project descriptions are detailed and clearly articulate the problem, solution, and impact, indicating strong communication abilities.