Personal Information
Organization / Workplace
Greater Seattle Area United States
Occupation
Director of Data Engineering
Industry
Education
Website
http://www.cwharland.com
About
Biophysicist turned Data Scientist with experience in a broad range of tech related consumer services from hardware, operating systems, and search engines to game consoles, quantitative marketing, and web apps.
My work has spanned statistics, experimentation, machine learning, analytics, and all sorts of data engineering problems. All with the goal of doing something useful for users, products, and company.
I am particularly interested in building recommendation engines and experimentation systems that help deliver better user experience and allow the company to make better product decisions utilizing all data available to them in a fast and easy manner.
Likes
(43)Polylog: A Log-Based Architecture for Distributed Systems
Kamil Sindi
•
5 years ago
Being Glue (Newer slides at https://noidea.dog/glue)
Tanya Reilly
•
5 years ago
Progression by Regression: How to increase your A/B Test Velocity
Stitch Fix Algorithms
•
5 years ago
Design Microservice Architectures the Right Way
Michael Bryzek
•
5 years ago
ICOTS 2018
Hilary Parker
•
5 years ago
Context Aware Recommendations at Netflix
Linas Baltrunas
•
5 years ago
Goto Berlin - Migrating to Microservices (Fast Delivery)
Adrian Cockcroft
•
9 years ago
Serverless in production, an experience report (LNUG)
Yan Cui
•
6 years ago
Medical advice as a Recommender System
Xavier Amatriain
•
6 years ago
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Amazon Web Services
•
8 years ago
Appraiser : How Airbnb Generates Complex Models in Spark for Demand Prediction
Yang Li Hector Yee
•
8 years ago
Word2vec in Theory Practice with TensorFlow
Bruno Gonçalves
•
6 years ago
Opinionated Analysis Development -- EARL SF Keynote
Hilary Parker
•
6 years ago
Automated Data Exploration: Building efficient analysis pipelines with Dask
ASI Data Science
•
6 years ago
Past, Present & Future of Recommender Systems: An Industry Perspective
Justin Basilico
•
7 years ago
Learning to Personalize
Justin Basilico
•
9 years ago
Learning a Personalized Homepage
Justin Basilico
•
10 years ago
Personalized Page Generation for Browsing Recommendations
Justin Basilico
•
9 years ago
Recommendations for Building Machine Learning Software
Justin Basilico
•
8 years ago
Lessons Learned from Building Machine Learning Software at Netflix
Justin Basilico
•
9 years ago
Recommendation at Netflix Scale
Justin Basilico
•
10 years ago
Discovering Persuasive Language through Observing Customer Behavior
Jason Kessler
•
6 years ago
Music Personalization At Spotify
Vidhya Murali
•
7 years ago
Building Data Science Teams
Jeremy Stanley
•
6 years ago
PyData London 2017 – Efficient and portable DataFrame storage with Apache Parquet
Uwe Korn
•
6 years ago
DataEngConf 2017 - Machine Learning Models in Production
Sharath Rao
•
7 years ago
How I learned to time travel, or, data pipelining and scheduling with Airflow
PyData
•
7 years ago
Feature Engineering
HJ van Veen
•
7 years ago
How to use Parquet as a basis for ETL and analytics
Julien Le Dem
•
9 years ago
Building a Machine Learning App with AWS Lambda
Sri Ambati
•
8 years ago
Personal Information
Organization / Workplace
Greater Seattle Area United States
Occupation
Director of Data Engineering
Industry
Education
Website
http://www.cwharland.com
About
Biophysicist turned Data Scientist with experience in a broad range of tech related consumer services from hardware, operating systems, and search engines to game consoles, quantitative marketing, and web apps.
My work has spanned statistics, experimentation, machine learning, analytics, and all sorts of data engineering problems. All with the goal of doing something useful for users, products, and company.
I am particularly interested in building recommendation engines and experimentation systems that help deliver better user experience and allow the company to make better product decisions utilizing all data available to them in a fast and easy manner.