Senior Data Scientist, Vox Media

• A track record of using data science to change a business decision, not just inform one, and the ability to walk us through the tradeoffs you made along the way. Cutting-edge ML is not required; choosing the right-sized solution is, even when that's rule-based logic
• Comfort owning a problem across its whole lifecycle: framing, building the dataset, modeling, shipping, and measuring what happened
• Fluency in Python and SQL, particularly in the context of data science, ML, and analytics
• Rigor with experiment design - you’ll identify experiments worth running, define details for tests, and draw conclusions and next steps from results
• High agency - You enjoy owning outcomes rather than individual tasks and are proactive about where you need help
• If you think you have what it takes, but don't meet every single point in our job posting, please apply with a cover letter to let us know how you believe you can bring your unique skills to the Vox Media team or get in touch!

Desirable

• As a Senior Data Scientist focused on our Subscriptions businesses, you’ll work on problems that fuel the next stage of growth. You will build tools, models, and frameworks that improve and act on our understanding of what our paying subscribers care about, what marketing strategies are most impactful, and how to engage our readers
• This role is on Vox Media’s Data team. We’re a small team that works unusually end-to-end. We own and maintain our data warehouse, have also put recommendation models in production, enable marketing personalization across channels, and own our experimentation platform
• Retention and engagement: Build the models and measurement that tell us which subscribers are at risk, why, and which interventions actually work, and then make those outputs drive real decisions in product and marketing
• Lifetime value: LTV models that tell us what acquisition channels to lean into, alongside integrations and automation to wire that into the places those spend decisions are made
• Personalization at scale: Extend our recommendation systems to new surfaces and form factors, and own the iteration loop
• Experimentation: Design and analyze tests that drive which of these experiences make it into production