Staff Machine Learning Engineer at Reddit
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Staff Machine Learning Engineer in Ads Recommendation / Personalization team at Reddit. Previously was ML engineer tech lead in Meta (Facebook) Ads Core Machine Learning team, driving end-to-end machine learning innovations on ads personalization, quality, delivery efficiency, and stability. Ph.D. in machine learning from Rutgers University. Passionate about applying AI/ML to solve real-world problems. Spend my 10+ year industry and academic career on the application of AI, machine learning, and deep learning to help businesses solve mission-critical problems.
Rutgers University–Newark
Doctor of Philosophy (Ph.D.), Machine Learning for Business Intelligence, Rutgers Business School
January 1, 2009 – January 1, 2014
Reddit, Inc.
Staff Machine Learning Engineer
August 1, 2022 – Present
Greater Seattle Area
Meta
Machine Learning Engineer
July 1, 2017 – July 1, 2022
Greater Seattle Area
Microsoft
Data Scientist II
December 1, 2015 – July 1, 2017
Greater Boston
PwC
Senior Associate
October 1, 2014 – October 1, 2015
New York City Metropolitan Area
Rutgers Business School
Instructor
September 1, 2011 – June 1, 2012
Newark, NJ
Rutgers Business School
Research Assistant
September 1, 2009 – June 1, 2014
Newark, NJ
Cultural Fit Analysis
The candidate's extensive experience across major tech companies (Reddit, Meta, Microsoft) and consulting (PwC) demonstrates adaptability to diverse corporate cultures. Their academic background and research contributions suggest a continuous learning mindset. The variety of projects, from ad personalization to financial fraud detection and retail recommendations, indicates a broad interest and ability to apply data analysis skills across different domains, aligning well with a dynamic data analyst role.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership in engineering teams, driving strategic direction, and project tactical execution, suggesting strong operational fit and leadership potential. The detailed descriptions of problem-solving and impact (e.g., cost reduction, efficiency improvement) indicate a results-oriented approach. Collaboration with XFN teams is also mentioned.