Lead high-impact data science initiatives across marketing, growth, and product, defining strategy, building models, and translating insights into recommendations.
Locations (2)
San Diego, California, United States
San Francisco, California, United States
Compensation
The annual U.S. salary range for this full-time position is $179,500 - $269,500.
Responsibilities
Lead high-impact data science initiatives across marketing, growth, and product, from problem definition through methodology, execution, interpretation, and recommendation.
Define org-level technical strategy, identify high-leverage opportunities, and establish best practices for experimentation, causal measurement, modeling, AI-assisted development, and decision science.
Design, analyze, and interpret experiments and quasi-experiments; build scalable frameworks to estimate incrementality, treatment effects, and long-term business value.
Develop ML, uplift, heterogeneous treatment effect, and adaptive experimentation models to improve targeting, segmentation, personalization, lifecycle optimization, audience strategy, budget allocation, and product decisions.
Partner with Marketing, Growth, Sales, Finance, and Product to evaluate acquisition, retention, ROI, user journeys, funnels, marketplace dynamics, donation conversion, and engagement.
Requirements
Master’s degree or Ph.D. in a quantitative field, or equivalent applied experience coupled with a bachelor’s degree
Lead data science and machine learning initiatives to improve student and business outcomes for an education technology company.
Responsibilities
Lead data science and AI/ML initiatives from problem definition through implementation and measurement
Turn ambiguous business problems into clear data science opportunities
Own technical direction of assigned initiatives
Work closely with Product, Engineering, Customer Experience, Partnership, and university partner teams
Identify new opportunities for machine learning, experimentation, causal inference, optimization, or AI
Requirements
Bachelor's or Master's degree in computer science, statistics, econometrics, mathematics, engineering, or another quantitative discipline, or equivalent relevant experience
Nice to have: PhD in a quantitative or technical discipline
