<div>About Faire Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. At Faire, were 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. </div>About this role Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of data scientists and machine learning engineers specialize in developing algorithmic solutions for notification and recommender systems, advertising attribution, and LTV predictions. Our ultimate goal is to empower local retail businesses with the tools they need to succeed. At Faire, the Data Science team is responsible for creating and maintaining a diverse range of algorithms and models that power our marketplace. We are dedicated to building machine learning models that help our customers thrive.  As a tech lead within the Retailer Growth Data Science team, you will develop ML systems that help activate new retailers and increase their engagement. Because our users are businesses, there is a wealth of information about them that can be consumed by ML models to personalize the new user experience, even in their first session. In addition to the huge number of products and brands on our platform, we offer an array of other value props, such as net terms, free shipping, and integrations. In order to help retailers get the most from this ecosystem, we use a variety of incentives and nudges, which can be optimized and personalized through ML solutions. You will also build ML systems to re-engage retailers through personalized marketing and develop intelligent recommendations that identify gaps in retailers product assortments to drive increased purchasing. These challenging problems offer exciting opportunities to create significant business impact through advanced personalization techniques. You will drive data science strategy and execution and you will work closely with cross-functional stakeholders and mentor senior data scientists to deliver results. Our team already includes experienced Data Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists and machine learning engineers, and you will help take us there! What you’ll do <ul> <li>Drive data science vision, strategy, and execution within Retailer Growth, using ML solutions to activate and engage more retailers on the platform</li> <li>Work with cross-functional stakeholders to develop end-to-end product solutions</li> <li>Mentor senior data scientists on Retailer Growth</li> <li>Use ML/AI to extract insights about retailers that can be used to personalize their first run experience at scale</li> <li>Optimize marketing and incentive spend through targeting and personalization</li> <li>Use experimentation and causal inference methods to measure the effectiveness of spend levers</li> <li>Solve challenging problems related to a two-sided marketplace</li> </ul> Qualifications <ul> <li>5+ years of industry experience using machine learning to solve real-world problems</li> <li>Experience with relevant business problems (personalization, incentives, e-commerce, CRM)</li> <li>Experience with relevant technical methods (unsupervised learning, NLP, LLMs, causal ML)</li> <li>Strong programming skills</li> <li>An excitement and willingness to learn new tools and techniques</li> <li>The ability to contribute to team strategy and to lead model development without supervision</li> <li>Experience as a tech lead, mentoring other data scientists / machine learning engineers</li> <li>Strong communication skills and the ability to work in a highly cross-functional team </li> </ul> Great to Haves: <ul> <li>Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields</li> <li>Previous experience in e-commerce personalization or incentive optimization</li> <li>Experience as a tech lead manager</li> </ul> Salary Range Canada: the pay range for this role is $196,000 CAD to $269,500 CAD per year. This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.<div>Effective January 2025, Faire employees will be expected to go into the office 2 days per week on Tuesdays and Thursdays. Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.  Applications for this position will be accepted for a minimum of 30 days from the posting date. Why you’ll love working at Faire <ul> <li>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 taking part in the founding process.</li> <li>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.</li> <li>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.</li> <li>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.</li> </ul> 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 Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our<a href="https://blog.faire.com/"> blog</a>. 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. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs.  To request reasonable accommodation, please fill out our <a href="https://docs.google.com/forms/d/e/1FAIpQLSczpI9me7nYKhunSC6AlagHKv75LHqfi-Qi2TP_3dpin0Uc9Q/viewform">Accommodation Request Form</a> (https://bit.ly/faire-form)</div>