Scot Fang
Data AnalystAds AI Ranking and Product
LinkedIn
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Ads AI Ranking and Product
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning (ML) Engineer and Natural Language Processing (NLP) Researcher. Passionate about ethical tech and sustainability.
UCLA
Master of Science (MSc), Computer Science - Machine Learning
January 1, 2012 – January 1, 2014
Cornell University
Bachelor's, China Asia Pacific Studies
January 1, 2005 – January 1, 2009
Senior Staff Software Engineer
February 1, 2025 – Present
Handshake
Director of Machine Learning
August 1, 2024 – February 1, 2025
Handshake
Tech Lead Manager - Machine Learning
June 1, 2022 – August 1, 2024
DiDi
Staff NLP Research Engineer
November 1, 2018 – June 1, 2022
Marina del Rey, California, United States
Senior Machine Learning Engineer
July 1, 2014 – November 1, 2018
Los Angeles
UCLA
Graduate Research Assistant - Computer Vision
January 1, 2014 – June 1, 2014
Software Engineer Intern - Computer Vision
June 1, 2013 – September 1, 2013
London Area, United Kingdom
Fastsoft (Acquired by Akamai Technologies)
TCP/IP Software Engineer
March 1, 2011 – September 1, 2012
Pasadena, California
China First Capital
Private Equity Associate
January 1, 2010 – July 1, 2010
Shenzhen, Guangdong, China
Lenovo
Consumer Business Group Intern
August 1, 2008 – December 1, 2008
World Resources Institute
Enterprise Intern
August 1, 2007 – December 1, 2007
Graduate Research - Data-Driven Causal Models in Medicine
January 1, 2013 – Present
Constructing causal networks for stroke treatment through statistical dependencies. Identifying clusters in heterogeneous patient populations and assessing the effectiveness of stroke scoring schema. Evaluating the causal effects of treatment to assist expert decision process.
IBM Jeopardy! Great Minds Challenge
December 1, 2012 – Present
(Click Title for Report Document) Blackbox classification competition given ~350 numeric features. Achieved performance comparable to the IBM Watson! system using EM Clustering coupled with additive LogitBoost.
Cultural Fit Analysis
The candidate has a diverse background, including experience in both large tech companies (Google, LinkedIn) and startups (Handshake, DiDi), as well as international experience (DiDi, China First Capital, Lenovo). The academic background includes both technical and humanities degrees, suggesting a broad perspective. However, the target role 'Data Analyst' is a significant shift from their recent senior/staff ML/AI engineering and leadership roles, which might indicate a potential mismatch in career trajectory or expectations for a pure analyst role. The projects demonstrate strong analytical capabilities, but the overall career path leans heavily towards ML engineering and leadership rather than traditional data analysis.
Soft Skills & Operational Fit
The candidate's experience in leadership roles (Director, Tech Lead Manager) suggests strong operational fit, including driving metrics, managing teams, and focusing on product quality. The project descriptions indicate analytical thinking and problem-solving abilities. However, specific soft skills like collaboration or stress handling cannot be directly assessed without psychometric test results or interview data.