Data Scientist Faire
San Francisco, CA

Job Description

Faire is using machine learning to change wholesale and help local retailers compete with Amazon and the big box stores. Our experienced data scientists and machine learning engineers are tackling recommendations, ranking, search, and underwriting, helping local retail thrive!

As a Data Scientist You Will:

As an early Data Scientist at Faire, you will be instrumental in helping build this new discipline in a high-growth startup. You will lead projects that derive value from our unique, rich, and rapidly growing data.

Problems you will be solving:

* Recommendation and ranking: Deploy recommendation and ranking algorithms to maximize sales of a maker's product to a given retailer; evolve this approach to include operations constraints in recommendation scores
* Underwriting: Evaluate the creditworthiness of retailers on our platform; use predictive modeling to dynamically assign credit limits that minimize default risk and maximize growth
* Growth: Give our Sales team superpowers by identifying leads with the highest likelihood of conversion; improve our signup/order funnels and landing pages with a/b testing
* Logistics: Predict quality of returns and help scale Fair logistics with peer-to-peer returns redistribution

Qualifications

* An advanced degree (MS or PhD) in mathematics, statistics, computer science, physics, operations research, economics or a related technical field
* 2+ years of industry experience in solving complex business problems using statistics and machine learning
* A consistent track record of performing data analysis using Python (numpy, pandas, scikit-learn, etc.) and SQL
* Strong communication skills and the ability to work with others in a closely collaborative team environment
* Relentless resourcefulness and the ability to thrive in a startup -- you're willing to do what needs to be done for the success of your team

Bonus/Nice to Haves:

* Prior experience in building and deploying recommendation systems for e-commerce business
* Familiarity with command line tools, version control systems (git), and software development

Technologies We Use and Teach:

* Python (numpy, pandas, sklearn) & R
* MySQL, Redshift
* Machine Learning (e.g. regression, ensemble methods, LightFM etc.)
* Statistics (Bayesian methods, experimental design, causal inference)

What We Offer:

* A small team, a relaxed and fun startup, world-class engineering talent, mentorship, and career growth
* We're growing fast. You will get in on the ground floor of something that's going to change an entire industry.
* Learn from the best. Our leadership team is experienced (eg., Google, Square) and led the development of one of the top 15 apps in the US app store (Square Cash).
* The business makes sense. It's straightforward, provides clear value to both store owners and to makers. You get to help change an industry for the better.
* It's beautiful. Work on something that's well-designed, following the footsteps of Square.

About Faire

Faire is an innovative online marketplace that uses machine learning to match local retailers with the makers of products that uniquely fit their stores. We are using the power of technology to connect makers and boutiques from all over the world, building a thriving community of over 10,000 small business owners.

Our mission is to empower entrepreneurs to chase their dreams. We believe entrepreneurship is a calling. It's a way of life that offers people a level of autonomy and fulfillment that's become difficult to find elsewhere in our economy. Most of our customers have left behind jobs they hated to take a chance on themselves. We want to make sure their boldness and self-belief gets rewarded.

This mission also extends to the way we are building the company. Every member of our team is an owner of the business and taking part in the founding process.

Faire is being built for entrepreneurs, by entrepreneurs.

Additional Information

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.