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Footwear Taxonomy Specialist, Shoefitr, Pittsburgh PA

Footwear Analyst 

Shoefitr aims to bridge the gap between in-store and online shopping by mimicking the tactile offline shopping experience. Our first product is software that helps online shoe shoppers find the best-fitting shoe when shopping online. We ask a shopper what shoe and size she currently wears, compare it to the shoe she wants to buy, and recommend the best-fitting size. We accomplish this with a database of internal shoe measurements acquired using 3D scanning technology. Our software is in use worldwide and our customer base is growing.
You may have read about us in TechCrunch, Time Magazine, CNet, or on Hacker Ne¬ws. We are a quickly growing company and are looking for leaders to help take the company to the next level and transform e-commerce.

About the Position

We are looking for someone who can work at approximately 25-28 hours during the week, Tuesday through Friday.  This position involves using categorization software to capture and group data Shoefitr gathers across the footwear industry.   You will be making categorization decisions and troubleshooting issues unique to the footwear industry.  This is a fun position where students or recent grads have an incredible opportunity to learn about 3D imaging technology, shoes, and startups.  Immediate start is desired. Hours are flexible within the 9AM to 6PM timeframe, Tuesday-Friday. We are located conveniently near Pitt and CMU on Oakland Ave.

About You

–  Demonstrated ability to learn new process quickly

–  Can work well with others

–  Ability to communicate questions and issues effectively

–  Computer Skills

–  Responsible and takes ownership of work

Bonus Points If

– You are fluent in a second language

– You enjoy the outdoors

Benefits

–  Flexible schedule

–  Fun work environment

–  Exposure to a fast growing startup.

Pay

$10 / hour, paid bi-weekly

Link to apply: http://shoefitr.theresumator.com/apply/job_20140331171900_O2N2IQKQAUH3WQ2P/Footwear-Taxonomy-Specialist.html?source=INDE