Principal Data Scientist / Machine Learner
Bellevue, WA

Job Description

OfferUp is on a mission to create the simplest, most trustworthy way to buy and sell locally. The free app for iOS and Android helps sellers quickly post items for sale with just a picture from their smartphones and lets buyers easily browse what's nearby. Now the largest mobile marketplace for local buyers and sellers in the U.S., OfferUp brings millions of people together to discover more value right where they are.

AT A GLANCE

* A top shopping app on iTunes and Google Play
* Geekwire App of Year
* 80+ Million Downloads

OfferUp is looking for a Principal Data Scientist to join our team.

We seek to delight our users with the safest, simplest and most effective local marketplace app on the planet. Your role as a Data Scientist will be to help OfferUp use the most cutting edge technology to maintain its market lead in customer trust and product enthusiasm.

OfferUp is a startup. Applicants are expected to understand the business and contribute their ideas and manage many of their own data extractions and data transforms.

Responsibilities:

* Build industry leading models in one of the following:
* Image Detection
* Natural Language Processing
* Localized Pricing
* Ad Optimization
* Fraud Detection
* Build data pipelines using:
* Spark
* Airflow



Requirements:

* 5+ years of demonstrated experience with machine learning or data science
* Expertise around scalable programming languages Java, Spark, Flink, TensorFlow, or other data science pipeline technologies.
* Hands on experience solving real business problems are required.
* Hands on experience solving real business problems are required. Ph.D. or Master's Degree in Computer Science, Mathematics, Statistics or another STEM-related field is also required.
* Must be eligible to work in the United States

Nice to Have:

* Experience working in e-commerce and mobile marketplaces
* Experience integrating code into a live production environment
* Some experience in some the following: entity extraction, semantics in text, image recognition, recommender systems, ranking, LDA, classification