- San Francisco, CA, USA
Optimizely is the world's leader in customer experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce and campaigns through its best in class experimentation software platform. By replacing digital guesswork with evidence-based results, Optimizely enables product and marketing professionals to accelerate innovation, lower the risk of new features, and drive up the return on investment from digital by up to 10X. Over 26 of the Fortune 100 companies choose Optimizely to power their global digital experiences. Optimizely’s impressive customer list includes eBay, FOX, IBM, The New York Times and many more global enterprises.
About the Job
As a Data Scientist at Optimizely, you’ll be responsible for translating developments from academia into tools that thousands of marketers, engineers, and data scientists use every day. You will join Optimizely’s Machine Learning/Statistics team, in charge of Stats Engine and machine learning methods that interface with Stats Engine such as Stats Accelerator, a novel multi-armed bandit algorithm for dynamic traffic allocation designed to deliver faster results.
You will work with our product and engineering teams to build new statistics features for all of Optimizely’s products. You will get on customer calls to articulate how we maintain statistical accuracy in our products as well as triage questions around statistical inference. You will educate internal and external stakeholders about fundamental as well as cutting edge statistics concepts via webinars, speaking engagements, blog posts, and white papers. You will conduct original research and publish results in academic and industry conferences.
PhD in Statistics or a closely related field, or 5+ years of equivalent industry experience in A/B testing and digital experimentation
Strong research interest and experience with design of experiments, randomized control trials, and inference, particularly aspects of high throughput testing such as multiple hypothesis testing, sequential testing, robustness, data mining of experiments
Experience with Python and/or Java preferred
Some experience in math education such as teaching, teaching assistantships, consulting, or conference talks
Some of our public work:
Stats Engine White Paper - http://pages.optimizely.com/rs/optimizely/images/stats_engine_technical_paper.pdf
Stats Engine Academic Paper - https://arxiv.org/abs/1512.04922
Bayesian vs Frequentist Statistics https://blog.optimizely.com/2015/03/04/bayesian-vs-frequentist-statistics/
Optimizely’s Stats Engine https://blog.optimizely.com/2015/01/20/statistics-for-the-internet-age-the-story-behind-optimizelys-new-stats-engine/
Approximate Counting and Statistical Significance https://medium.com/engineers-optimizely/approximate-counting-and-statistical-significance-two-great-ideas-that-dont-play-nice-2bd643287644#.6uyxiytlr
CODE @ MIT 2016: A/B Testing in a Changing World
INFORMS 2015: Can I Take a Peek? Continuous Monitoring of A/B Tests
CODE @ MIT 2014: Can I Take a Peek? Continuous Monitoring of A/B Tests
All your information will be kept confidential according to EEO guidelines.