Introduction to the team:
The members of Wayfair’s Data Science group come from a range of highly quantitative backgrounds (think astrophysics, economics, cognitive science, and operations research, engineering and math).
The projects that our teams work on are driven from the ground up – we look for entrepreneurial individuals that want to take ownership over their own agenda and thrive in a collaborative team environment.
Our Computer Vision team uses the latest in the research community to build algorithmic intelligence of Wayfair’s millions of images for our customers, suppliers, and in-house scientists. Imagery and style is at the core of Wayfair’s catalog offering.
Check out some of our work here: https://tech.wayfair.com/team-data-science/
The Work We Do Encompasses
Many of our projects are new and mostly projects that have never been worked on before.
- Modular algorithm design – develop re-usable building blocks for quantitative models, leveraging high parallel, distributed machine learning and advanced data analysis techniques
- Algorithm platform engineering – architect, build, and maintain technical platforms for our algorithmic engines to run at scale, both for online and offline needs
- Influencing business decisions – relentlessly leverage our work and encourage adoption across our business partners, to drive real business value
- Data mining – work together with data scientists to uncover deep insight hidden in our vast repository of raw data, and provide tactical guidance on how to act on findings
As part of this role, you will use your software engineering skills to help build scalable machine learning models that drive value across multiple areas of the business. You’ll work within Wayfair’s latest big data technology infrastructure to develop innovative and new machine learning engineering capabilities.
- Currently enrolled in a PhD program at a top-tier institution with a strong academic track record.
- 2+ years of software engineering experience or advanced degree in quantitative field w/ material exposure to coding (e.g. mathematics, economics, computer science, physics, neuroscience, operations research etc.)
- Intuitive sense of how to architect high performance distributed computing systems for machine learning & tie them to business problems
- Strong background in machine learning and parallel processing pipelines
- High comfort level with programming, e.g. languages such as Python, R, Scala, etc
- Intense intellectual curiosity – strong desire to always be learning
- Analytical, creative, and innovative approach to solving open-ended problems
- Highly collaborative, team-player attitude
Wayfair is one of the world’s largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, or in our warehouses or offices throughout the world, we’re reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If you’re looking for rapid growth, constant learning, and dynamic challenges, then you’ll find that amazing career opportunities are knocking.
No matter who you are, Wayfair is a place you can call home. We’re a community of innovators, risk-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair – and world – for all. Every voice, every perspective matters. That’s why we’re proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic information.
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- Salary Offer 0 ~ $3000
- Experience Level Junior
- Total Years Experience 0-5
- Dropdown field Option 1