Data Scientist - Intern
Faire, San Francisco, CA, United States
Responsibilities
- You will be able to work in a cross-functional setup on core Machine Learning problems.
- The areas you might be working on include content-based recommendations, personalized retrieval and ranking, user signals building, etc.
- Define, plan and execute cutting-edge machine learning or other new algorithms that will be A/B tested with guidance from a manager or technical lead.
- Communicate project objectives and results clearly, both within the group as well as to the broader team.
- Tackle complex issues inherent in managing a two-sided marketplace.
Qualifications
- We are open to currently enrolled Master’s & PhD students and recent Master’s & PhD graduates, who have an academic focus in Computer Science, Operations Research, Statistics, Econometrics or a related technical field.
- Hands-on experience with real datasets and familiarity using Python, sklearn, numpy, pandas, and SQL.
- Familiarity with various machine learning techniques and statistical methodologies (Bayesian methods, experimental design, causal inference).
- A track record of developing end-to-end Data Science projects and/or producing academic papers that have been showcased in top journals or conferences.
Company information: 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 ecommerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally.
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