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Machine Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I like answering questions with data and writing algorithms to harness information for action.
Stanford University
M.S. Statistics
January 1, 2015 – January 1, 2017
Stanford University
B.S. Mathematical & Computational Science
January 1, 2011 – January 1, 2015
Accenture
Machine Learning Engineer
September 1, 2017 – Present
San Francisco Bay Area
Vision Imaging Science and Technology Lab, Brains in Silicon (Wandell & Boahen Labs)
Research Assistant
September 1, 2016 – June 1, 2017
Scotiabank Global Banking and Markets
Data Science & Analytics Analyst
May 1, 2016 – August 1, 2016
Fujitsu Laboratories of America
Researcher
October 1, 2015 – March 1, 2016
AgTech Startup
Data Science Intern
June 1, 2015 – October 1, 2015
Factual Inc
Data Engineering Intern
June 1, 2014 – September 1, 2014
The Stanford Mobile and Social Computing Research Group
Research Assistant
June 1, 2013 – September 1, 2013
Assessing the Effect of Recommender Systems on the Evolution of User-Content Network Polarization
January 1, 2016 – Present
We assess the interaction between content recommendation systems and increasing political polarization of the American public. To do so, we built a simulation framework to test the effect of the type of recommendation system, distribution of political beliefs across the user friend graph, and probability that a user will like a recommended article, on polarization in article readership over time. The framework is built in Python, and simulations can easily be run from the command line.
Cultural Fit Analysis
The candidate has a diverse background spanning research, startups, and large corporations, indicating adaptability. The personal project on recommender systems and polarization shows an interest in complex, socially relevant data problems. However, the target role is 'Data Analyst' while much of the experience leans towards 'Data Scientist' or 'Machine Learning Engineer', which might indicate a slight mismatch in scope or expectation for the target role's typical responsibilities. The breadth of experience suggests a strong learning aptitude.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest strong analytical and problem-solving skills. Roles involving client presentations and working with domain experts indicate good communication and collaboration abilities. The research assistant roles and personal project highlight initiative and independent work.