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Software Engineer, Machine Learning at Facebook
I earned my Ph.D. degree in Computer Science from University of Toronto, with 8 years of experience in Natural Language Processing, Text Mining and general Artificial Intelligence problems. I have a general interest in all kinds of data science tasks and problems, ranging from predictive modeling, recommender systems, semantic search, and large-scale data processing. I'm particular confident and proficient with coding and development in Python-based data science toolkit, and have hands-on experience with the latest big data technologies, i.e., HDFS, HIVE, ElasticSearch, MongoDB, and Apache Spark. I am also proficient in a wide range of programming languages, with the capability for full-stack development and implementation of Data Science products.
University of Toronto
Doctor of Philosophy (PhD), Computer Science
January 1, 2011 – January 1, 2014
University of Toronto
Master of Science (M.Sc.), Computer Science
January 1, 2009 – January 1, 2011
Shanghai Jiao Tong University
Bachelor of Engineering, Information Security
January 1, 2005 – January 1, 2009
Meta
Software Engineering Manager
May 1, 2025 – Present
Meta
Software Engineering Manager
March 1, 2024 – June 1, 2025
Meta
Machine Learning Engineer
September 1, 2021 – February 1, 2024
Adeptmind
Senior Machine Learning Scientist
May 1, 2020 – September 1, 2021
Adeptmind
Machine Learning Scientist
March 1, 2018 – May 1, 2020
The Globe and Mail
Lead Data Scientist
November 1, 2016 – January 1, 2018
Greater Toronto Area, Canada
Workopolis
Lead Data Scientist
December 1, 2015 – October 1, 2016
Greater Toronto Area, Canada
Workopolis
Data Scientist
January 1, 2015 – November 1, 2015
Greater Toronto Area, Canada
University of Toronto
PhD
January 1, 2011 – December 1, 2014
Toronto
eBay
Intern at research lab - China
May 1, 2008 – April 1, 2009
Shanghai, China
Content-based Recommender System
July 1, 2015 – Present
Developed our second-version personalized recommender system using the content-based methodology. For each job seeker, from their previous on-site behaviour, I extracted a synthesized profile for the seeker. This job seeker profile captures the seeker’s general interest in jobs, in terms of the locations, job categories, industries, occupations, job levels, etc. The recommender system therefore recommends the jobs best aligned with the seeker’s profile. Successfully solved the computational challenges by delving into the framework of Apache Spark. Measured from two test email campaign targeting lapsed job seekers, the recommender achieves 15% open rate, 35% click-through-rate, and 22% apply rate, which is the most effective email campaign in engaging job seeker traffic.
Semantic Job Search
April 1, 2015 – Present
Worked with Technology team as the subject matter expert in the process of migrating Job Search engine from DBSight to ElasticSearch. By mining user behaviour data, I delivered a semantic data layer which reveals the true meaning behind each search term. With these “semantics” embedded into ElasticSearch queries, the job search engine becomes much more intelligent, which can go beyond pure lexical matching and understands the human intention behind each search. With the new search engine, we gained 20% more traffic from job alerts, 30% higher apply rate, and reduced failed seeker rate from 40% to 3%.
Job Matching
March 1, 2015 – Present
Developed a predictive model which predicts whether a given candidate’s resume is qualified for a given job description. Benchmarked against internal recruiting data, the model is able to quickly screens out 50% of the unqualified candidates with 95% precision, which doubles the current efficiency of human screening.
National Occupation Code Classification System
February 1, 2015 – Present
Developed an API classifying an input job description into one of the official National Occupation Code, which includes 500 standardized job positions as the basis of Labour Force Survey and statsCan data. The classifier achieves 90% accuracy and provides an essential way to canonicalize all kinds of unpredictable variations in job titles. The classifier later becomes the fundamental basis of many other data science projects.
Cultural Fit Analysis
The candidate has a diverse background spanning academia (University of Toronto), large tech companies (Meta, eBay), and industry-specific roles (Workopolis, The Globe and Mail). The projects demonstrate a strong focus on data-driven solutions for business problems, which aligns well with a results-oriented culture. The transition from individual contributor to leadership roles (Lead Data Scientist, Software Engineering Manager) indicates ambition and a willingness to take on more responsibility. However, the target role is 'Data Analyst', which might be a step down from their recent managerial and senior scientist roles, potentially indicating a mismatch in career trajectory or expectations. The projects are highly relevant to data analysis, but the depth of experience suggests a more senior data scientist or machine learning engineer role.
Soft Skills & Operational Fit
The candidate's experience as a Lead Data Scientist and Software Engineering Manager suggests strong leadership, team management, and coaching abilities. The project descriptions highlight problem-solving skills and a results-oriented approach. The academic background indicates strong analytical and research capabilities. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.