Help & Supportcareers@gravityer.com
Machine Learning @ Disney
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 scientist with 7+ years of experience, currently transitioning from a brief career break taken to support and resolve family health matters, with full availability for new opportunities. Drove innovation and business impact through cross-functional data science projects, deploying scalable end-to-end data products, and managing machine learning model lifecycles. Awarded multiple patent grants. Experienced in building data pipelines, model prototyping & productionization, dashboard development, and experiment design.
University of Southern California
Bachelor of Arts, Music
N/A – Present
Columbia University
Master of Science, Data Science
N/A – Present
University of Houston
Postbaccalaureate Studies, Mathematics and Computer Science
N/A – Present
The Walt Disney Company
Lead Machine Learning Engineer
July 1, 2025 – Present
San Francisco Bay Area · Hybrid
Thumbtack
Staff Applied Scientist
June 1, 2022 – January 1, 2025
Remote
Discord
Senior Data Scientist
April 1, 2021 – May 1, 2022
San Francisco Bay Area · Remote
Electronic Arts (EA)
Senior Data Scientist
November 1, 2019 – April 1, 2021
Redwood City, California, United States · Hybrid
Baker Hughes, a GE company
Data Scientist
January 1, 2019 – November 1, 2019
San Ramon, CA
Viacom
Data Science Intern
August 1, 2018 – December 1, 2018
Greater New York City Area
NASA Langley Research Center
Machine Learning and Computer Vision Intern
June 1, 2018 – August 1, 2018
Hampton, VA
Open iT, Inc.
Data Science Intern
May 1, 2017 – August 1, 2017
Houston, TX
University of Houston
Undergraduate Researcher
January 1, 2017 – June 1, 2017
Houston, TX
CCTV News International
Music Composer and Foreign Expert
January 1, 2013 – July 1, 2014
Beijing, China
Beijing Dance, Drama, and Opera
Composer and Orchestrator
February 1, 2011 – June 1, 2012
Beijing, China
Freelance
Composer and Music Producer
May 1, 2010 – May 1, 2016
Beijing, China
Spotify Song Lyric Analysis
May 1, 2018 – June 1, 2018
This is a comprehensive analysis of almost six decades of mainstream American music. With data combined from the annual Billboard Hot 100 rankings, lyrics scraped from various web sources, and proprietary audio features curated by Spotify, the dataset was rich with possibilities. In addition to performing some traditional analysis like visualizing patterns over time or exploring various correlation structures, I found answers to questions like, "What lyrics are most typical of each decade?" The answer isn't pretty.
Extending DeepER: Deep Learning for Entity Resolution
February 1, 2018 – April 1, 2018
Entity resolution is a difficult problem in data management. For example, given two datasets, one of products from Amazon and another of products from Walmart, how can we tell when both datasets are referencing the same product if they use different naming conventions and item descriptions? DeepER is a framework that leverages deep learning and word embeddings to address these issues, and this project examines various methods for extending DeepER's capabilities.
Implementations of Machine Learning Algorithms
January 1, 2018 – Present
The notebooks in this repository have been re-purposed from the homework in John Paisley's Machine Learning class at Columbia and also include experiments of my own.
Amazon Film & TV Recommendations
September 1, 2017 – December 1, 2017
This final group project for Brett Vintch's Personalization Theory course surveys the effectiveness of various classic collaborative filtering and matrix factorization algorithms for predicting consumers' ratings for products in Amazon's Film & TV catalogue based on their past ratings. We also implemented algorithms from scratch, including PLSI (probabilistic latent semantic indexing) and an approximate nearest neighbors method called cosine-based LSH (locality sensitive hashing).
TweetRater
July 1, 2017 – Present
TweetRater is a personal deep learning project in which a convolutional neural network (CNN) predicts whether a tweet contains offensive language, hate speech, or neither. Other models tested include multinomial Naive Bayes with term frequency-inverse document frequency vectors and a regular multilayer perceptron. The CNN was deployed as a web application where users can input their own tweets.
Likely Voter Prediction
April 1, 2017 – Present
This was an extracurricular machine learning project in which an ensemble classifier predicts whether an election poll respondent is likely to be a voter or non-voter. These predictions are then used to filter survey samples for improved polling accuracy. Models tested include random forests, support vector machines, logistic regression, and adaptive boosting.
Sequence Models
DeepLearning.AI
June 24, 2026 – Present
Structuring Machine Learning Projects
DeepLearning.AI
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
DeepLearning.AI
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
DeepLearning.AI
June 24, 2026 – Present
Improving Deep Neural Networks
DeepLearning.AI
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career progression from music composition to a highly technical ML Engineer role demonstrates significant adaptability, a strong learning mindset, and a diverse background. Their involvement in various personal projects and contributions to open-source initiatives (implied by 'Implementations of Machine Learning Algorithms') suggest a passion for the field beyond their professional duties. The breadth of industries (tech, gaming, energy, media, government) and types of problems tackled (recommendations, fraud detection, pricing, user behavior) indicate a versatile individual who can thrive in dynamic environments. The leadership roles and mentoring experience align well with a collaborative and growth-oriented culture.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight leadership, mentoring, cross-functional collaboration, and strategic planning, indicating strong soft skills relevant for senior roles. Their work on accelerating experimentation and defining long-term strategies suggests a proactive and impactful operational fit. The diverse project portfolio also indicates adaptability and problem-solving capabilities.