Staff Data Scientist - Machine Learning, Personalization
San Francisco, CA
Faire Wholesale, Inc.
Posted 1 month ago
Faire is an online wholesale marketplace built on the belief that the future is local — there are over 1M independent retailers in the U.S. and Canada doing more than twice the revenue of Walmart and Amazon combined. At Faire, we're using the power of tech, data, and machine learning to connect a thriving community of over 100,000 brands and local retailers around the world. Picture your favorite boutique in town — we help them discover the best products to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses can compete with these big box and ecommerce giants. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement.
Staff Data Scientist - Machine Learning, Personalization
Faire is using machine learning to change wholesale and help local retailers compete with Amazon and big box stores. Our experienced data scientists and machine learning engineers are developing solutions related to discovery, ranking, search, recommendations, logistics, underwriting, and more - all with the goal of helping local retail thrive.
The Data Science team owns a wide variety of algorithms and models that power the marketplace. We care about building machine learning models that help our customers thrive.
As the Staff Data Scientist - Machine Learning on the Personalization team you’ll be responsible for developing machine learning-powered ranking models and adding personalization to our search and discovery. You’ll determine answers to questions like, how relevant is this product to this retailer and how can we infer that in real-time? Can we create real-time embeddings of users' interests and use them to power ranking? How can we improve purchase flow? What techniques can we use to enable our A/B tests to more quickly converge when dealing with low sample sizes?
Our team already includes experienced Data Scientists from Square, Quora, TripAdvisor, Wealthfront and Overstock; and the head of the team is an experienced manager from Airbnb who is also a professor of machine learning at UC Berkeley. Faire will soon be known as a top destination for data scientists and machine learning, and you will help take us there!
You’re excited about this role because…
- You’ll be able to work on cutting-edge ranking problems combining a wide variety of data about our retailers, brands and products
- You want to use machine learning to help local retailers and independent brands succeed
- You want to be a foundational member of a fast growing company
- You like to solve challenging problems related to a two-sided marketplace
- 6+ years of industry experience using machine learning to solve real-world problems, and a history of accomplishment and advancement in your Data Science career
- Experience with learning to rank (pointwise, pairwise, listwise models and understanding the trade-offs associated with each)
- Strong programming skills. The tooling/language isn’t important. (e.g., numpy, pandas, scikit-learn, R, Julia, etc.)
- An excitement and willingness to learn new tools and techniques
- Experience with relational databases and SQL
- The ability to contribute to team strategy and to lead model development without supervision
- Strong communication skills and the ability to work with others in a closely collaborative team environment
Great to Haves:
- Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields
- Experience with Neural Network models for ranking
- Experience with de-biasing and new learning to rank models
- Ability to quickly implement state of the art algorithms from an academic paper
What We Offer:
- We’re growing fast. You will get in early on something that’s going to be special.
- We’re funded by the best VCs in the world.
- Our founding team is experienced and led the development of one of the top 25 apps in the US App Store (Square Cash).
- Great technology stack. It’s fun to work with the best tools and the latest software.
- The business makes sense. It’s straightforward and provides clear value to both retailers and brands. You get to help change an entire industry for the better.
- It’s beautiful. Work on something that’s well-designed, following the footsteps of Apple and Square.
Why you’ll love working at Faire:
- We are entrepreneurs: We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is an owner of the business and taking part in the founding process.
- We are using tech and machine learning to level the playing field: We are using the power of technology and data to connect brands and boutiques from all over the world, building a thriving community of over 100,000 small business owners.
- We build products our customers love: Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie - not steal a piece from it. Running a small business is hard work, but using Faire makes it easy.
- We are curious and resourceful: We always find a way to get the job done and come up with creative solutions to whatever problems are standing in our way. People at Faire are insatiably curious. We lead with curiosity and data in our decision making and reason from a first principles mindset.
Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have offices in San Francisco, Kitchener-Waterloo, and Salt Lake City. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. To learn more about Faire and our customers, you can read more on our blog.
Faire is being built for entrepreneurs, by entrepreneurs.
Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression.
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