Marketplace Quality Data Scientist

2 months ago

Toronto, Ontario, Canada Faire Full time

About Faire

Faire is revolutionizing the wholesale industry by leveraging machine learning and data insights to empower local retailers. Our mission is to level the playing field for small businesses, enabling them to compete with giants like Amazon and big box stores.

Our highly skilled team of data scientists and machine learning engineers develop cutting-edge solutions for notification and recommender systems, advertising attribution, and LTV predictions. We are dedicated to building machine learning models that help our customers thrive.

The Role: Marketplace Quality Data Scientist

As the Marketplace Quality lead within the Brand Data Science team, you will play a crucial role in enhancing the quality of retailers' experiences with products and brands on Faire. Your focus will be on reducing egregious issues such as counterfeit products, fulfillment delays, damaged and missing items, and poor quality products.

You will develop data science models and algorithms to identify and mitigate these issues, ensuring a positive and reliable marketplace for our retailers.

Responsibilities:

  • Design, develop, and implement data-driven solutions to improve marketplace quality.
  • Analyze large datasets to identify patterns and trends related to product quality and fulfillment issues.
  • Develop predictive models to forecast potential quality problems and proactively address them.
  • Collaborate with cross-functional teams, including engineering, product, and operations, to implement solutions and drive continuous improvement.
  • Stay up-to-date on the latest advancements in data science and machine learning techniques relevant to marketplace quality.

Qualifications:

  • Master's degree or PhD in Computer Science, Data Science, Statistics, or a related field.
  • Proven experience in developing and deploying data science models for real-world applications.
  • Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning.
  • Proficiency in programming languages such as Python or R.
  • Excellent analytical and problem-solving skills.

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