This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
DataEngineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “DataEngineers of Netflix” series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. Kevin, what drew you to dataengineering?
Part of our series on who works in Analytics at Netflix?—?and and what the role entails by Alex Diamond This Q&A aims to mythbust some common misconceptions about succeeding in analytics at a big tech company. Within a few months I’d picked up BI tools, predictive modeling, and data ingestion/ETL.
At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with dataanalytics and dataengineering, we comprise the larger, centralized Data Science and Engineering group.
While automating IT practices can save administrators a lot of time, without AIOps, the system is only as intelligent as the humans who program it. This requires significant dataengineering efforts, as well as work to build machine-learning models. Big data automation tools. Monitoring automation is ongoing.
64% of the respondents took part in training or obtained certifications in the past year, and 31% reported spending over 100 hours in training programs, ranging from formal graduate degrees to reading blog posts. To nobody’s surprise, our survey showed that data science and AI professionals are mostly male. Salaries by Gender.
Margaret leads the worldwide solution architect program for sustainability, and gives an excellent talk on how customers should think about optimizing their workloads. STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager DataEngineering and Michael Floyd AWS Head of Sustainability Solutions.
Curious to learn more about other Data Science and Engineering functions at Netflix? To learn about Analytics and Viz Engineering, have a look at Analytics at Netflix: Who We Are and What We Do by Molly Jackman & Meghana Reddy and How Our Paths Brought Us to Data and Netflix by Julie Beckley & Chris Pham.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content