Collectors have long enlisted dealers or auction houses to help resell their art holdings because such insiders typically have up-to-date pricing data and access to potential buyers. Now, in the latest challenge to the art world's status quo, a team led by former Sotheby's rainmaker Adam Chinn plans to launch a peer-to-peer digital marketplace later this month that will invite collectors to sell high-end art to each other, directly and anonymously. Listings in an early version of the site, called LiveArt Market, include an Andy Warhol "Rorschach" from 1984 valued around $200,000 and Jack Pierson's 2009 sign, "Glory," valued around $85,000. The move comes as all sorts of art-world players rethink the traditional ways art gets traded online, from former Christie's auctioneer Loïc Gouzer's Fair Warning auction app to the proliferation of digital platforms selling NFT artworks. Even as the art world's attention increasingly pivots back to in-person art events including fairs, online sales of luxury goods remain robust and some top industry dealmakers see a bigger market opportunity in finding fresh ways to sell art to collectors accustomed to shopping for art online.

About Our Team: At our core, we are an analytics company. The insights we gain from our analyses guide our strategic path forward as we grow revenue, enter new markets, and strengthen our customer relationships. The Data Science team proactively leads and collaborates to identify the most valuable problems to solve, constructs a Roadmap to delivery, and executes our plan. All of the outputs of the Data Science team models will feed into the Product portfolio at DRIVIN, aiding tens of thousands of internal and external stakeholders in their decision-making processes. About Our Candidate: Can think for themselves and discover new and insightful ways to solve difficult problems without having a clear roadmap laid out Can communicate effectively with data science teammates and non-technical audiences alike Can deliver quality code in an Agile framework that ships to a production environment Has confidence, hustle, energy, and drive – accountability is key and the impact of your work is crucial to our success What You Will Be Doing: Own the machine learning engineering required to efficiently operate computer vision, optimization, and other data driven decision systems Partner with our computer vision and data scientists to improve model performance and responsiveness Work in an Agile environment with team members, delivering solutions quickly and continuously exploring ways to improve our results Work closely with colleagues in Engineering, Product, Operations, and Sales to structure problems and understand the impact across various departments within the company What You Need to Be Successful: Candidates tend to have at least a Bachelor's Degree in a quantitative field, but if you can explain how your experience and background can be leveraged as a senior contributor to a data science team we are all ears Significant experience (roughly 5 years) in a Data Science, Deep Learning, Machine Learning Engineering, Computer Vision, or Data Engineering position building data products in a production environment Experience in the full project lifecycle from requirements gathering to proof of concept to production delivery Experience designing and implementing machine learning models that are production-ready Familiarity with designing and implementing models for computer vision such as neural networks using deep learning frameworks like PyTorch or TensorFlow Experience coding with Python and SQL Familiarity with CUDA and C Experience with developing within a cloud environment.