Hi, I’m
Daniel Ryu
Milpitas,CA
Summary
My progression into Data Science began at a startup, where pursuing my interests & opportunities led me through multiple acquisitions and industries. My skills and knowledge developed organically as the business needs arose, until I decided to improve stepwise - completing my Master's in Data Science at UC Berkeley. Through it all, I've demonstrated resilience, quickness, and versatility that I consider my most important assets. I love to engage in unfamiliar ideas and opportunities, and am now looking for my next challenge.
Overview
7
years of professional experience
Work History
Tutor
Evernote
Data Scientist, Turbine
Growth Analyst
11.2021 - 02.2023
Job overview
- Leading the newest feature's analysis - ideation, exploration, refinement, and storytelling at a company All-Hands meeting - Planning robust event tracking, to arm our analyses and ultimately guide the product roadmap - Preparing, analyzing, and presenting A/B split tests to the Product team, to drive design optimization - With the launch of several new features, helping set KPIs and designing self-service Tableau dashboards to monitor results - Using Machine Learning techniques on an imbalanced dataset to classify free users by their likelihood to convert to paid
- Digital
02.2021 - 11.2021
Job overview
- Key Analyses: - Joining performance by OS version to my scraped minimum OS versions from Play Store - Setting best practice and automating the analysis of incrementality using a two-proportion T-test
Criteo
Data Analyst
10.2018 - 01.2021
Job overview
- In addition to the responsibilities at Manage, I became the reporting and analytics expert for the commercial team: - Writing and maintaining scripts to support external and internal reporting needs - Creating data visualization dashboards (Tableau, Zeppelin) - User behavior and Inventory insights for optimization
- Manage.com
Campaign Manager
08.2016 - 10.2018
Job overview
- Manage provides in-app mobile advertising solutions
- In Oct 2018, Manage was successfully acquired by Criteo to expand the company's App Install product
- Analysis across exchanges, campaigns, publishers, etc, finding and executing optimizations for client and internal ROI - Owning internal & external reporting tools for performance, user, and revenue analysis (Tableau, Excel, SQL)
Education
OOP in Python, data analysis
- Research Design and Applications for Data and Analysis
- Statistics for Data Science (R modeling, descriptive/explanatory regression analysis)
- Fundamentals of Data Engineering (Docker, Kafka, Spark, processing streaming data)
- Applied Machine Learning (Neural Nets, Decision Trees, kNN, Naive Bayes, SVMs, Clustering, Dimensionality Reduction)
- Machine Learning at Scale
University Overview
(Distributed Computing: MapR, Spark), ML Systems Engineering (ML APIs, Docker, Kubernetes, Microservices vs. Monolith, CI/CD)
Created using Resumonk - Online Resume Builder
- Computer Vision (Fourier, Convolution, Pyramids, HOGs, Compression, Image Recognition)
Capstone Project: We developed a tool for farmers to decide what crops to grow based on a ensemble model of yield (XGBoost)
and revenue (Huber
classes, control structures, recursion/algorithms)
University of California
Masters of Information and Data Science
01.2022
University of California
BA from Economics, Programming
01.2015
University Overview
Economics classes focusing on technology/e-commerce companies, regression analysis, policy
- Programming coursework: OOP fundamentals (data types
Skills
- SQL, Python (pandas, numpy, sklearn, matplotlib), Spark, Unix, Tableau, A/B Testing, statistics, Databricks, Google Cloud
- And hip hop beats (soundcloudcom/danielryu)
- Created using Resumonk - Online Resume Builder
Timeline
Evernote Data Scientist, Turbine Growth Analyst
11.2021 - 02.2023
02.2021 - 11.2021
Data Analyst
Criteo
10.2018 - 01.2021
Campaign Manager
08.2016 - 10.2018
Tutor
OOP in Python, data analysis
- Research Design and Applications for Data and Analysis
- Statistics for Data Science (R modeling, descriptive/explanatory regression analysis)
- Fundamentals of Data Engineering (Docker, Kafka, Spark, processing streaming data)
- Applied Machine Learning (Neural Nets, Decision Trees, kNN, Naive Bayes, SVMs, Clustering, Dimensionality Reduction)
- Machine Learning at Scale
classes, control structures, recursion/algorithms)
University of California
Masters of Information and Data Science
University of California
BA from Economics, Programming