Vanessa Feng

Data Analyst

https://www.opentalent.in/vanessa-feng

Key Strengths

Extensive experience in Machine Learning and Data Science, particularly in recommender systems, semantic search, and predictive modeling.
Proven ability to deliver measurable business impact through data science initiatives (e.g., 15% open rate, 35% click-through-rate, 22% apply rate for recommender system; 20% more traffic, 30% higher apply rate for semantic search).
Strong academic background with a PhD and MSc in Computer Science from the University of Toronto, specializing in Computational Linguistics and supervised machine learning.
Experience leading data science teams and coaching members in Data Science and Analytical skills.
Familiarity with large-scale data processing frameworks like Apache Spark.

Cultural & Operational Fit

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.

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About

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.

Top Skills

Natural Language ProcessingComputational linguisticsArtificial IntelligenceMatlabPythonC++Machine LearninglatexJavaProgrammingData AnalysisSQLStatisticsMysqlResearchC++Apache SparkhivemapreduceElasticsearchMongoDBJavaScriptJqueryD3.jsAlgorithmsBig DataHadoopComputer ScienceData scienceAnalyticsKubernetesKerasDeep LearningTensorFlowPredictive ModelingBig Data AnalyticsFull Stack DevelopmentReinforcement Learning

Education

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

Experience

Projects

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.

Key Strengths

Extensive experience in Machine Learning and Data Science, particularly in recommender systems, semantic search, and predictive modeling.
Proven ability to deliver measurable business impact through data science initiatives (e.g., 15% open rate, 35% click-through-rate, 22% apply rate for recommender system; 20% more traffic, 30% higher apply rate for semantic search).
Strong academic background with a PhD and MSc in Computer Science from the University of Toronto, specializing in Computational Linguistics and supervised machine learning.
Experience leading data science teams and coaching members in Data Science and Analytical skills.
Familiarity with large-scale data processing frameworks like Apache Spark.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit