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Senior Data Scientist / Machine Learning Engineer - Search, Personalization, Ads - job 1 of 2

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, 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 the Role: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, ads, logistics, underwriting, and more - all with the goal of helping local retail thrive.As a member of the Discovery Data team you’ll be responsible for one of the following areas:• Search: Optimizing search through query understanding, retrieval, ranking, post-ranking and whole-page optimization with state-of-art technologies including embeddings, graph learning, deep learning and large language models (LLMs).• Personalization: Personalizing recommendation surfaces through embeddings, near-real-time / streaming signals, explore-exploit, and diversification.• Ads / Sponsored Products: Joining a newly established team building Ads Delivery and Advertiser Optimization from the ground up and tackling challenges in ads targeting, retrieval, prediction/ranking, bidding, pacing, and auction design.Our team already includes experienced Data Scientists and Machine Learning Engineers from Uber, Airbnb, Square, Facebook, LinkedIn and Pinterest. We're a lean, talented team with high opportunity for direct product impact and ownership.You’re excited about this role because…• You’ll be able to work on cutting-edge search / personalization / ads 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 team member of a fast growing company• You like to solve challenging problems related to a two-sided marketplaceQualifications:• 4 years of industry experience using machine learning to solve real-world problems• Experience and strong understanding of search / personalization / ads for product development• Strong programming skills• An excitement and willingness to learn new tools and techniques• Experience with deep learning• 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 environmentGreat to Haves:• Highly recommended: Master’s or PhD in Computer Science, Statistics, or related STEM fields• Ability to quickly implement state of the art algorithms from an academic paperSalary Range:California: the pay range for this role is $177,000 - $244,000 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.This role will be in-office on a hybrid schedule - Faire employees will be expected to go into the office 2 days per week on Tuesdays and Thursdays, effective the week of January 13, 2025. Additionally, in-office roles will have the flexibility to work remotely up to 4 weeks per year.Applications for this position will be accepted for a minimum of 30 days from the posting date.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 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 Toronto, London, and New York. 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.Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form)
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What You Should Know About Senior Data Scientist / Machine Learning Engineer - Search, Personalization, Ads, Faire

Join Faire as a Senior Data Scientist / Machine Learning Engineer, where your expertise can genuinely impact the retail landscape! Based in the vibrant hub of San Francisco, CA, you'll be part of a dedicated team unleashing the power of technology and data to empower independent retailers across the globe. At Faire, we firmly believe in supporting local businesses, and as part of our Discovery Data team, you’ll tackle exciting challenges in areas like search optimization, personalization, and ad delivery. Picture yourself optimizing search algorithms using advanced techniques such as deep learning and large language models, or personalizing recommendations based on real-time data. You’ll collaborate with a talented crew of like-minded professionals from top companies such as Uber and Facebook, all driven by a passion for problem-solving and innovation. If you're excited about using machine learning to create solutions that help local businesses thrive and eager to join a dynamic, fast-growing team that values entrepreneurial spirit, Faire is the perfect place for you. With comprehensive responsibilities that range from search ranking to developing advertising strategies, your ideas will contribute to empowering small retailers to compete with larger entities like Amazon. So, if you’re ready to take on this thrilling journey, come make a difference with us and help keep the local shopping community thriving!

Frequently Asked Questions (FAQs) for Senior Data Scientist / Machine Learning Engineer - Search, Personalization, Ads Role at Faire
What are the key responsibilities of a Senior Data Scientist / Machine Learning Engineer at Faire?

As a Senior Data Scientist / Machine Learning Engineer at Faire, you'll focus on leveraging machine learning for search optimization, personalization, and ad delivery. This includes tasks like enhancing query understanding, implementing state-of-the-art algorithms, and developing personalized recommendation systems. The role calls for a significant degree of collaboration with a talented team and the requirement to contribute independently to model development and team strategy.

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What qualifications should I have to apply for the Senior Data Scientist / Machine Learning Engineer position at Faire?

To qualify for the Senior Data Scientist / Machine Learning Engineer role at Faire, candidates should possess at least four years of industry experience in using machine learning for real-world applications, with a solid grasp of search, personalization, and advertising principles. Proficiency in programming and deep learning frameworks is crucial, alongside exceptional communication skills and a passion for learning new tools and technologies.

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How does Faire use machine learning to support independent retailers?

Faire utilizes machine learning to create innovative solutions that empower independent retailers to thrive. By developing robust systems for search optimization, personalized recommendations, and effective ad targeting, Faire enhances the visibility and competitiveness of local businesses, enabling them to connect more effectively with their customers and compete against larger corporations in the retail space.

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What is the work culture like for a Senior Data Scientist / Machine Learning Engineer at Faire?

The work culture at Faire is characterized by a strong entrepreneurial spirit, teamwork, and innovation. Employees are encouraged to explore creative solutions to complex challenges and leverage data-driven decision-making. With a commitment to professional growth and collaboration, team members benefit from the diversity of experiences within a lean, talented team while making impactful contributions.

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Is the Senior Data Scientist / Machine Learning Engineer role at Faire remote or in-office?

The Senior Data Scientist / Machine Learning Engineer position at Faire is offered on a hybrid schedule. Employees will be expected to come into the office two days a week, alongside the flexibility of working remotely for up to four weeks per year. This hybrid approach fosters collaboration while accommodating modern working preferences.

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Common Interview Questions for Senior Data Scientist / Machine Learning Engineer - Search, Personalization, Ads
What experience do you have with machine learning algorithms applicable to search and personalization?

When answering this question, share specific projects where you've implemented machine learning algorithms for search optimization or personalized recommendations. Discuss the methodologies used, the data leveraged, and the outcomes achieved to demonstrate your hands-on experience and understanding of these technologies.

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Can you describe a challenging problem you solved in your previous role as a data scientist?

In your response, choose an instance that showcases your problem-solving abilities and impact on a project. Explain the nature of the challenge, the analytical approach taken, the tools used, and the results you achieved, highlighting how your skills as a Senior Data Scientist contributed to overcoming obstacles.

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How do you stay updated on the latest developments in machine learning technologies?

Convey your commitment to continuous learning by mentioning resources such as academic journals, online courses, and relevant conferences or workshops. Share specific examples of how you've applied new knowledge in previous work, reflecting your proactive approach to keeping up-to-date with the rapidly evolving ML landscape.

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What methods would you utilize to improve search results and personalization at Faire?

Discuss various strategies you would employ, such as implementing cutting-edge algorithms, gathering diverse data sources, and testing user feedback through A/B testing. Keep your response focused on how these methods would enhance the relevance and efficiency of search results and personalization efforts.

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How would you approach developing a new recommendation system?

Outline a systematic approach to building a recommendation system, covering stages like understanding user needs, data collection, algorithm selection, and iterations based on user feedback. Mention specific techniques relevant to the role, such as collaborative filtering or content-based filtering, reinforcing your technical competence.

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Give an example of a time when you worked closely with a cross-functional team.

Highlight a particular project in which you collaborated with teams such as engineering, product management, or marketing. Discuss how effective communication, shared goals, and the alignment of strategies led to the successful completion of the project and what you learned from this collaborative experience.

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What are your preferred programming languages and tools for data analysis?

List your favorite programming languages such as Python or R and popular machine learning frameworks like TensorFlow or PyTorch. Explain the context in which you have used these tools, why you prefer them for data analysis, and how they have aided you in your previous roles as a data scientist.

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How do you evaluate the performance of your machine learning models?

Discuss various metrics you use to assess model performance, such as accuracy, F1 score, precision, recall, or AUC-ROC. Provide an example where you implemented these metrics in practice and any decisions you made based on evaluation results to improve model performance.

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What techniques would you use for feature engineering in your projects?

Describe the process you follow for feature engineering, including techniques such as domain knowledge application, extracting features from existing data, or using machine learning models for feature selection. Provide examples where you successfully improved model performance through thoughtful feature engineering.

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How do you handle discrepancies between model predictions and actual results?

In your response, emphasize the importance of diagnosing the root causes of discrepancies, using data visualization to analyze patterns, and applying additional validation techniques. Discuss how you would iterate on your models based on insights gained, showcasing your problem-solving and analytical skills.

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Faire is an online wholesale platform that leverages technology to empower local boutiques and independent businesses globally, facilitating their access to unique worldwide products and driving positive economic impact in their communities.

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Full-time, hybrid
DATE POSTED
December 8, 2024

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