Root
Director of Data Science, Performance Marketing
Remote (United States) · Director
Sponsorship not specified$250k-$300kDetected 25 days ago
Machine LearningData SciencePerformance MarketingCommunication
About the role
- At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
- This includes optimizing bidding strategies, accelerating model iteration and deployment, and advancing applied R&D to improve how we capture and convert demand at scale.
- We will continue to have our headquarters in Columbus to give more flexibility and more choice about how we live and work.
Responsibilities
- At Root, the Data Science team owns the majority of all marketing capital allocation, using quantitative methods to test, maintain, and enhance our marketing strategies across numerous distribution channels.
- As a Director of Data Science, Performance Marketing, you will drive continuous improvement of marketing strategies, channels, and operational processes while supporting the exploration of novel opportunities.
Requirements
- The Opportunity We believe that a disruptive insurance company must have a principled quantitative framework at its foundation.
Nice to have
- Insurance industry experience Experience driving performance in large-scale customer acquisition programs
Compensation
- This helps us create a more personal and engaging experience for both you and our interviewers.
- Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy.
- If you have any concerns, feel free to let us know once you are contacted.
- We're happy to talk it through.
Benefits
- You will lead a team of data scientists, machine learning engineers, and marketing specialists who design, deploy, and evolve the systems that power Root's performance marketing engine.
This listing is sourced directly from Root's careers page and normalized into a canonical job model.