Applied Science- Growth

What you'll do

• Develop intent, conversion, and customer lifetime value models to guide acquisition and engagement.
• Build marketing measurement and optimization systems for allocating spend across channels, markets, and time; use causal inference to estimate incremental impact.
• Partner with business and technical teams to productionize models and evaluate and improve their effects on decisions and business outcomes.

What they ask for

• Strong Python skills and experience building maintainable software and contributing to production ML systems.
• Experience taking predictive models from problem definition and training through deployment, evaluation, and iteration; strong foundation in classification and statistical modeling.
• Applied experience in causal inference and experimental design, including incremental-effect estimation and reasoning about confounding, selection bias, and uncertainty.
• Ability to evaluate predictive performance and business outcomes, work with imperfect data and ambiguous questions, and communicate clear recommendations.

Nice to have

• Experience with marketing measurement and optimization, including MMM, MTA, and budget allocation; or with customer acquisition, lifecycle marketing, personalization, customer lifetime value, and next-best-action systems.
• Background in real estate, housing, or marketplaces; familiarity with PySpark.

Tools

Python, PySpark

AI focus

Build and deploy machine-learning models and decision systems for customer intent, conversion, lifetime value, marketing measurement, and spend optimization.