Principal Research Data Scientist, iSpot.tv

• Advanced degree (Master’s or Ph.D.) in mathematics, economics, statistics, computer science, physics, social sciences, or another quantitative field strongly preferred
• 8+ years of professional experience in data science, statistical modeling, and/or machine learning, with a track record of delivering production-grade analytical solutions at scale
• Demonstrated expertise in advanced statistical methods, experimental design, causal inference, and/or large-scale predictive modeling
• Proven ability to influence technical direction and gain buy-in from senior stakeholders across business, product, and engineering functions

Desirable

• As a Principal Research Data Scientist at iSpot, you will serve as a thought leader and technical authority, driving innovation and shaping the future of TV measurement, attribution, and creative testing
• You’ll work on some of the most challenging and high-impact problems in the TV advertising analytics space, leading the design of advanced methodologies, overseeing complex modeling projects, and guiding cross-functional initiatives that push the boundaries of what is measurable in a constantly evolving marketing landscape
• The Research Data Science team partners closely with Product, Engineering, and Client teams to create scalable, scientifically rigorous solutions that power iSpot’s industry-leading audience measurement and attribution products
• Technical Leadership & Vision – Define and drive the research agenda for audience measurement, attribution, and creative assessment, ensuring methodological rigor and long-term scalability
• Model Development & Deployment – Architect, develop, and operationalize advanced statistical and machine learning models that deliver novel insights from massive, high-dimensional viewing and demographic datasets
• Innovation & Methodology Advancement – Introduce and evaluate cutting-edge data science techniques; publish or present findings internally and externally to strengthen iSpot’s industry reputation
• Cross-Functional Partnership – Collaborate closely with Product and Engineering leadership to translate business challenges into advanced analytical solutions, and ensure successful integration into production systems
• Mentorship & Standards – Serve as a mentor to other data scientists, elevating the technical bar across the team through code reviews, best practices, and peer development
• Strategic Problem-Solving – Provide guidance on data architecture and pipeline design in collaboration with Engineering to ensure analytical efficiency, scalability, and data quality