Job Description:
• Build closed-loop decision systems using machine learning for pricing and other consumer-business decisions
• Estimate causal responses and quantify uncertainty
• Choose actions, generate useful information, observe outcomes, and update policies
• Evaluate challenger policies and deploy systems within guardrails
• Work across causal and heterogeneous treatment-effect modeling, uncertainty estimation, contextual bandits, active learning, sequential decision-making, policy learning, constrained optimization, counterfactual evaluation, experimentation, production ML infrastructure, monitoring, and automated deployment
• Produce measurable economic lift in controlled experiments
• Generalize systems across businesses and safely automate commercial decisions
• Apply the decision-system capability across a portfolio of businesses and increasingly broad commercial decisions
Requirements:
• Exceptional technical ability and judgment
• Experience in several of machine learning and statistical modeling, causal inference and experimentation, recommendation, advertising, pricing, marketplace, credit, or other decision systems
• Experience with bandits, reinforcement learning, optimization, active learning, uncertainty estimation, counterfactual evaluation, or production ML systems
• Python and SQL proficiency
• Experience working with large behavioral datasets
Benefits: