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Director, AI and ML for Global Markets at Barclays
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Virginia
Master’s Degree, Data Science
January 1, 2015 – January 1, 2016
North Carolina State University
Bachelors, Chemical Engineering
January 1, 2008 – January 1, 2012
Barclays
Director, AI and ML for Global Markets
June 1, 2026 – Present
New York, New York, United States
Morgan Stanley
Executive Director, Machine Learning Researcher
January 1, 2023 – June 1, 2026
Morgan Stanley
Vice President, Machine Learning Researcher
January 1, 2019 – January 1, 2023
Morgan Stanley
Machine Learning Researcher
July 1, 2016 – January 1, 2019
ERM: Environmental Resources Management
Associate Engineer
July 1, 2012 – July 1, 2015
New York City Metropolitan Area
North Carolina Solar Center
Energy Intern
June 1, 2011 – May 1, 2012
North Carolina State University
Resident Advisor
August 1, 2009 – June 1, 2010
North Carolina State University
Research Assistant
November 1, 2008 – June 1, 2011
Text Mining Approaches for Predicting Religious Violence-Capstone
May 1, 2016 – Present
> Computed document signals through multiple text mining approaches including LDA, sentiment analysis, semantic density analysis, network quantification and syntactic parsing > Predicted the belief elasticity of religious groups through SVM, Random Forests, and Artificial Neural Networks > Published results in 2016 SIEDS proceedings
Music Genre Classification Through Machine Learning
May 1, 2016 – Present
> Classified 30 second audio clips into 10 genres using the samples’ mel-frequency cepstrum coefficients > Reviewed prediction efficacy of Random Forests, Artificial Neural Networks, and Restricted Boltzmann machines
Contextual Shifts of Political Buzzwords Through Semantic Density Analysis
May 1, 2016 – Present
> Measured the shifts in semantic density of presidential speeches using context vectors > Developed new semantic clustering method (k-nearest context vectors) to analyze topical shifts in buzzwords
NBA Game Predictions through Logistic Regression and Monte Carlo Methods
December 1, 2015 – Present
> Developed player performance simulator using logistic regression, Monte Carlo and bootstrapping > Compared performance against Las Vegas odds for fourth quarter of 2014-2015 season
Market Sentiment Analysis Through Twitter Mining
August 1, 2015 – Present
> Extracted real-time Twitter data through Twitter API and tick-level stock data through web scraping > Compared stock performance to key word spikes in live tweets using the efficient markets hypothesis
Intro to Computational Finance with R
DataCamp
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong background in quantitative analysis and machine learning, particularly within the finance industry. Their project portfolio demonstrates a breadth of interest in applying ML to various domains, from social science to finance and entertainment. The career progression from an Associate Engineer to Director of AI and ML indicates ambition and a drive for growth. The target role of ML Engineer aligns well with their professional experience and educational background. The diversity of projects, while personal, shows initiative and a continuous learning mindset. However, the lack of explicit team-based project descriptions or contributions makes it difficult to fully assess collaborative cultural fit.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong analytical and problem-solving aptitude. Their progression through senior roles at major financial institutions suggests leadership potential and the ability to manage complex projects. The early career experience in engineering and research also points to a methodical and detail-oriented approach. However, specific soft skill demonstrations (e.g., teamwork, communication in a team setting) are not explicitly detailed in the provided data.