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Sr. Data Scientist Engineer
I am a passionate Data Scientist and Machine Learning Engineer with 10+ years of experience turning data into actionable insights and building innovative machine learning models to solve complex business problems. My expertise spans data analysis, predictive modeling, natural language processing, and scalable machine learning solutions. I enjoy collaborating with cross-functional teams to drive data-driven decision-making and deliver impactful results. Throughout my career, I have: Designed and deployed machine learning models for applications such as fraud detection, customer retention, and pricing optimization. Worked with large datasets, utilizing tools like Python, PySpark, SQL to uncover patterns and drive strategic decisions. Combined technical expertise with business acumen to translate analytical findings into meaningful outcomes.
Drexel University
Doctor of Philosophy (PhD), Information Studies
January 1, 2009 – January 1, 2015
Wuhan University
Master's degree, Information Science/Studies
January 1, 2007 – January 1, 2009
Wuhan University
Bachelor's degree, Information Management and Information System
January 1, 2003 – January 1, 2007
Verizon
Sr. Data Science Engineer
March 1, 2025 – Present
Cox Automotive Inc.
Senior Data Scientist
December 1, 2021 – December 1, 2024
Cox Automotive Inc.
Data Scientist II
November 1, 2019 – December 1, 2021
T-Mobile
Machine Learning Engineer
May 1, 2017 – November 1, 2019
Bellevue
TCL Research America
Visiting Researcher
June 1, 2015 – December 1, 2015
San Francisco Bay Area
Drexel University
Research Assistant
February 1, 2013 – April 1, 2015
Greater Philadelphia
Drexel University
Research Assistant
February 1, 2010 – September 1, 2013
Greater Philadelphia
Comparison of Nonnegative Matrix Factorization with Constraints and Regularizations
February 1, 2015 – April 1, 2015
• Correlations between constraints and regularizations of Nonnegative Matrix Factorization are investigated in order to efficiently build clustering models that conform to the data features and their intrinsic structures.
Sparse regularized Nonnegative Matrix Factorization for data clustering on manifolds
February 1, 2014 – December 1, 2014
• A novel sparse regularized model is proposed for cluster analysis in sparse data. • A multiplicative algorithm is designed for the model and the experiments are implemented on benchmark data sets.
The Colorectal Cancer Health Education website construction for Pennsylvania Department of Health
July 1, 2013 – September 1, 2013
• Developed the website with html, css, javascript, jquery, jsp, java servelt, and MicrosoftSQL server.
Document Clustering with An Augmented Nonnegative Matrix Factorization Model
February 1, 2013 – October 1, 2013
• Motivated by optimizing clustering results, a novel nonnegative matrix factorization model is designed, by adding regularization on both data manifold and feature manifold, to reserve the data structure during the matrix factorization process. • A multiplicative algorithm is designed for the model and its effectiveness is tested. • Experiments are applied to both benchmark datasets and the data extracted from an online social network website.
Investigating Main Subject Areas and Influential Scholars from 19 iSchools in North America
March 1, 2010 – June 1, 2010
• ISchools in North America are classified by their major subject areas, and those major subject areas are identified, using hierarchical clustering analysis and k-means. • The most influential faculty members and their influential features are investigated, using clustering analysis and indicators from citation analysis.
PCEN (Pennsylvania Cancer Education Network) data warehouse
February 1, 2010 – June 1, 2012
• Developed and edited implementations for the warehouse with HTML, CSS, JavaScript, JSP and Java Servlet. • Conducted usability studies on the site and provided troubleshooting assistance as needed.
Cultural Fit Analysis
The candidate's project diversity, ranging from academic research in matrix factorization and clustering to industry applications in churn prediction, anomaly detection, and natural language understanding, indicates adaptability and a broad interest in various ML domains. The progression through Data Scientist and Sr. Data Science Engineer roles, coupled with an ML Engineer role at T-Mobile, shows a clear career path aligned with the target ML Engineer role. The academic background and research focus suggest a strong inclination towards innovation and continuous learning, which is beneficial for a dynamic technical environment.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight collaboration with cross-functional teams (Emerging Business team, National Capacity Planning team, Drexel School of Public Health) and delivering insights to stakeholders, indicating good communication and teamwork skills. The ability to lead projects resulting in awards and patents suggests initiative and problem-solving capabilities. The detailed project descriptions imply a structured approach to problem-solving and execution.