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Software Engineer at Facebook
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Assessing your cultural and operational fit
NYU Center for Data Science
Master’s Degree, Data Science
January 1, 2014 – January 1, 2016
University of California, Berkeley
Exchange Student, Statistics
January 1, 2011 – January 1, 2011
The University of Hong Kong
Bachelor's Degree, Statistics & Economics
January 1, 2010 – January 1, 2014
Zhejiang University
Preparatory Year, Science
January 1, 2009 – January 1, 2010
Staff Software Engineer
July 1, 2020 – Present
Staff Machine Learning Engineer
May 1, 2019 – July 1, 2020
Senior Machine Learning Software Engineer
February 1, 2018 – April 1, 2019
Software Engineer
July 1, 2016 – February 1, 2018
Text IQ
Research Intern
February 1, 2016 – May 1, 2016
New York
NYU Center for Data Science
Junior Data Scientist for Software Working Group
September 1, 2015 – December 1, 2015
New York
Cablevision
Data Science Intern
June 1, 2015 – August 1, 2015
New York
The University of Hong Kong
Part-time Research Assistant
September 1, 2012 – May 1, 2013
Hong Kong SAR
China International Capital Corporation
Summer Analyst - Sales and Trading
July 1, 2012 – August 1, 2012
Shenzhen
Analysis and Visualization of NYC Taxi Data
February 1, 2015 – May 1, 2015
The primary goal of our project is to explore New York taxi trip data from 2011 to 2013, as well as its relationship with the corresponding weather data. Besides statistics about trips, we focused on retrieving information about drop-offs and pick-ups at certain location in certain time under certain weather condition, and detecting unusually busy areas based on that. For data analysis, we used Hadoop to process and summarize three years of taxi data whose size is in hundreds of Gigabytes and used Python for further analysis based on output from Hadoop joined with weather data. For website and visualization, we built on open source frameworks such as Flask, Bootstrap and CartoDB.
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
February 1, 2015 – May 1, 2015
Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.
Keyword Extraction for Stack Exchange Questions
February 1, 2015 – May 1, 2015
In this project we experimented two approaches on solving keyword extraction problems. A traditional method that transforms input text into TF-IDF representations and trains linear classifiers on the feature space; and a deep learning approach that utilizes word embedding representations of the text and builds a deep convolutional neural network on the inputs. Deep learning approach shows potential but traditional method sets a high baseline.
Cultural Fit Analysis
The candidate's background is heavily skewed towards Machine Learning Engineering and Software Engineering, with a strong focus on building and deploying ML systems. While they have a Master's in Data Science and early experience in data analysis, their recent roles are more aligned with ML engineering than a pure Data Analyst role. The projects demonstrate a strong academic foundation in data science and ML. The transition to a Data Analyst role might represent a shift in focus, and their experience with large-scale ML systems could bring valuable insights, but the direct alignment with a 'Data Analyst' role, which typically focuses more on business intelligence, reporting, and descriptive analytics, is not perfectly matched. Their experience is more advanced than typical Data Analyst roles, potentially indicating an overqualification or a desire for a different career path.
Soft Skills & Operational Fit
The candidate's experience at major tech companies like Twitter and Facebook, including leadership roles (Tech Lead, Staff Engineer), suggests strong operational fit, problem-solving abilities, and collaboration skills. The project descriptions indicate a structured approach to complex problems. However, without specific psychometric test results, a detailed assessment of soft skills like stress handling and team collaboration is limited.