Data ScientistSame Seats. Better Prices.Among the fastest growing technology companies, TickPick is reshaping the secondary ticket marketplace, putting money back in the wallets of live event-goers. Since inception, we have saved our customers over $55 million in service fees.
Our BestPrice Guarantee backs up our promise to deliver better prices than our competition.For the last five years, TickPick has been named a Deloitte Technology Fast 500 award winner and has landed on lists of Inc. 5000's and Crain's New York Business' Fast 50.If you are passionate about concerts, sports, or theater, and want to see your skills and experience have a direct impact on a fast growing company, TickPick is the place for you. We are building a lean, high-output engineering team committed to providing the most innovative, transparent, and cost-effective ticket marketplace in the industry.We're looking for a data scientist to help our marketplace business continue to innovate and grow.
An ideal candidate will have professional experience building machine learning models that are used to predict business outcomes — such as customer propensity to spend, churn prediction, price forecasting, or other relevant areas — and, just as importantly, experience operationalizing those models so they run reliably in production without becoming a maintenance burden over time.You should have a curious mind and an interest in how a marketplace business optimizes its growth, with a vision for how to ensure innovation, transparency, and cost-effectiveness at scale. You should also be comfortable partnering with stakeholders across the business — communicating your findings, building buy-in, and seeing your work through to real production impact. If you are looking to join a growing team within a growing company that's making data a priority, this is the role for you.
You'll have the opportunity to touch many aspects of the business.Core responsibilities include:Support the organization as we experiment with new product features and strategies, helping to forecast and understand the impact of these changes.Build, deploy, and maintain machine learning models in production — operationalizing them in a sustainable, maintainable way.Collaborate with product, growth, and development teams to identify opportunities to incorporate a data science approach to product / feature development, and to drive experiments and initiatives through to a decision and into production.Assist with analysis of large volumes of transactional data to identify opportunities to create efficient, transparent, and low-cost marketplace interactions that drive growth.