Execute Director of Data Science and Machine Learning at JPMorgan Chase & Co.
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Passionate and innovative scientist with a strong desire for applying data-driven techniques to address complex problems. Experienced in machine learning, natural language processing, big data analytics, and application development.
University of Toronto
Doctor of Philosophy (PhD), Chemical Physics
January 1, 2004 – January 1, 2009
University of Toronto
Master of Science (MSc), Chemical Physics
January 1, 2003 – January 1, 2004
Sharif University of Technology
Bachelor of Science (BSc), Chemistry
January 1, 2000 – January 1, 2003
National Organization for Development of Exceptional Talents
High School Diploma, Physical Sciences
January 1, 1993 – January 1, 2000
JPMorgan Chase & Co.
Executive Director of Data Science and Machine Learning
January 1, 2022 – Present
JPMorgan Chase & Co.
VP of Data Science and Machine Learning
January 1, 2018 – January 1, 2022
JPMorgan Chase & Co.
Lead Data Scientist and Machine Learning Engineer
November 1, 2014 – January 1, 2018
Columbia University
Research Scientist
February 1, 2011 – November 1, 2014
New York, NY, USA
University of Toronto, Advisor: Nobel Laureate Prof. John Polanyi
Postdoctoral Fellow
January 1, 2009 – January 1, 2011
Toronto, ON, Canada
University of Toronto, Advisor: Nobel Laureate Prof. John Polanyi
Research and Teaching Assistant
September 1, 2003 – January 1, 2009
Toronto, ON, Canada
Predictive Modeling of Income and Time-Series Data
July 1, 2014 – Present
Retrieved census data about employment and demographic information from the web using Curl. Performed exploratory data analysis and classification modeling (logistic regression, decision tree) using R, as well as trend analysis and forecasting of time-series data. Achieved 86% accuracy in predicting the income based on the census data.
Sentiment Analysis of Tweets
June 1, 2014 – Present
Retrieved live tweets from the Twitter streaming API using Python. Performed text mining using the Natural Language Toolkit (NLTK package) and evaluated the sentiment scores of tweets using a lexicon of positive and negative words. Analyzed the public opinion based on the geographical location, and evaluated the mood of each US State.
Clustering Analysis of Dopant Distribution in Graphene
March 1, 2012 – Present
Lead a collaborative team project involving 12 scientists and engineers on the distribution of dopants in graphene. Parsed and processed large and noisy datasets of atomic coordinates in Python, and developed a clustering model in Matlab to analyze the spatial distribution of atomic dopants. Discovered a novel segregation of large-scale dopant domains with important implications for the design of electronic materials—results published in JACS
Machine Learning
Coursera
June 24, 2026 – Present
Introduction to Data Science
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's extensive experience in a large financial institution (JPMorgan Chase & Co.) and academic research roles suggests adaptability to structured and research-intensive environments. The personal projects demonstrate initiative and a broad interest in applying data analysis techniques across different domains (census data, social media, materials science). However, the projects are personal and lack explicit mention of team collaboration or business impact beyond the 'Clustering Analysis' project, which was a collaborative team project. The target role is 'Data Analyst', which might be a step down from an 'Executive Director of Data Science and Machine Learning' role, potentially indicating a mismatch in career trajectory or expectations, or a desire for a more hands-on analytical role. The breadth of skills is good for a data analyst, but the depth in specific business-oriented tools or platforms for a typical data analyst role is not explicitly detailed in the projects.
Soft Skills & Operational Fit
The candidate's career progression at JPMorgan Chase & Co. from Lead Data Scientist to Executive Director suggests strong leadership, project management, and operational skills within a corporate environment. Their academic background and research experience indicate strong problem-solving, critical thinking, and collaboration abilities. The project descriptions are clear and demonstrate an ability to articulate complex technical work.