Staff Data Scientist & TLM - Fraud Prevention

About Faire

Faire is an online wholesale marketplace built on the belief that the future is local — there are over 2 million independent retailers in North America and Europe doing more than $2 trillion in revenue. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants.

By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.

About this role

Ensuring a quality marketplace is the top trust consideration for our retailer population. At Faire, we strive to create a high-quality environment where retailers can shop confidently without any concerns for fraudulent activity or any other adverse experience that undermines their trust or well-being.

As a leading member of the Data Science team, you’ll collaborate closely with our product and operations team to build an end-to-end system to manage marketplace quality and reduce risk. You’ll determine answers to questions like, how to build a scalable machine learning system to detect various marketplace quality violations? How can we build the right human-in-the-loop operational system to scalably verify and enforce violation behaviors? What is the right balance between carrots (incentive mechanisms to reward good behavior) and stick (platform policy enforcement and penalty) to achieve the best outcome? How can we measure the impact of quality improvement on retailer trust and purchase behavior? 

Our team already includes experienced Data Scientists and Machine Learning Engineers from Google, Uber, Airbnb, Square, Facebook, and Pinterest. 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 want to employ a suite of powerful methodologies, including machine learning, optimization, and causal inference to build a systematic data science solution 
• You want to work in a highly cross-functional team and provide thought leadership in designing a strategy
• You want to be a foundational team member of a fast-growing company
• You like to solve challenging problems related to a two-sided marketplace

Qualifications

• 5+ years of industry experience using machine learning to solve real-world problems, and a history of accomplishment and advancement in your Data Science career 
• Multiple years of experience as the primary technical lead for cross-functional projects involving fraud detection and other risk management use cases (people management experience a nice-to-have)
• Strong execution programming skills
• 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 

• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields

Faire’s flexible work model aims to meet the needs of our diverse employee community by making work more flexible, connected, and inclusive. Depending on the role and needs of the team, Faire employees have the flexibility to choose how they work–whether that’s mainly in the office, remotely, or a mix of both. 

Roles that list only a country in the location are eligible for fully remote work in that country or in- office work at a Faire office in that country, provided employees are located in the registered country/province/state. Roles with only a city location are eligible for in-office or hybrid office work in that city. Our talent team will work with candidates to determine what locations and roles are eligible for each option.

Why you’ll love working at Faire

• We are entrepreneurs: Faire is being built for entrepreneurs, by 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 technology and data to level the playing field: We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 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: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality.

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 headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Salt Lake City, Atlanta, Toronto, London, New York, LA, and Sao Paulo. To learn more about Faire and our customers, you can read more on our blog.

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.